finance research topics 2021

Research Topics & Ideas: Finance

120+ Finance Research Topic Ideas To Fast-Track Your Project

If you’re just starting out exploring potential research topics for your finance-related dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of finance-centric research topics and ideas.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable education-related research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

Overview: Finance Research Topics

• Corporate finance topics
• Investment banking topics
• Private equity & VC
• Asset management
• Hedge funds
• Financial planning & advisory
• Quantitative finance
• Treasury management
• Financial technology (FinTech)
• Commercial banking
• International finance

Corporate Finance

These research topic ideas explore a breadth of issues ranging from the examination of capital structure to the exploration of financial strategies in mergers and acquisitions.

• Evaluating the impact of capital structure on firm performance across different industries
• Assessing the effectiveness of financial management practices in emerging markets
• A comparative analysis of the cost of capital and financial structure in multinational corporations across different regulatory environments
• Examining how integrating sustainability and CSR initiatives affect a corporation’s financial performance and brand reputation
• Analysing how rigorous financial analysis informs strategic decisions and contributes to corporate growth
• Examining the relationship between corporate governance structures and financial performance
• A comparative analysis of financing strategies among mergers and acquisitions
• Evaluating the importance of financial transparency and its impact on investor relations and trust
• Investigating the role of financial flexibility in strategic investment decisions during economic downturns
• Investigating how different dividend policies affect shareholder value and the firm’s financial performance

Investment Banking

The list below presents a series of research topics exploring the multifaceted dimensions of investment banking, with a particular focus on its evolution following the 2008 financial crisis.

• Analysing the evolution and impact of regulatory frameworks in investment banking post-2008 financial crisis
• Investigating the challenges and opportunities associated with cross-border M&As facilitated by investment banks.
• Evaluating the role of investment banks in facilitating mergers and acquisitions in emerging markets
• Analysing the transformation brought about by digital technologies in the delivery of investment banking services and its effects on efficiency and client satisfaction.
• Evaluating the role of investment banks in promoting sustainable finance and the integration of Environmental, Social, and Governance (ESG) criteria in investment decisions.
• Assessing the impact of technology on the efficiency and effectiveness of investment banking services
• Examining the effectiveness of investment banks in pricing and marketing IPOs, and the subsequent performance of these IPOs in the stock market.
• A comparative analysis of different risk management strategies employed by investment banks
• Examining the relationship between investment banking fees and corporate performance
• A comparative analysis of competitive strategies employed by leading investment banks and their impact on market share and profitability

Private Equity & Venture Capital (VC)

These research topic ideas are centred on venture capital and private equity investments, with a focus on their impact on technological startups, emerging technologies, and broader economic ecosystems.

• Investigating the determinants of successful venture capital investments in tech startups
• Analysing the trends and outcomes of venture capital funding in emerging technologies such as artificial intelligence, blockchain, or clean energy
• Assessing the performance and return on investment of different exit strategies employed by venture capital firms
• Assessing the impact of private equity investments on the financial performance of SMEs
• Analysing the role of venture capital in fostering innovation and entrepreneurship
• Evaluating the exit strategies of private equity firms: A comparative analysis
• Exploring the ethical considerations in private equity and venture capital financing
• Investigating how private equity ownership influences operational efficiency and overall business performance
• Evaluating the effectiveness of corporate governance structures in companies backed by private equity investments
• Examining how the regulatory environment in different regions affects the operations, investments and performance of private equity and venture capital firms

Asset Management

This list includes a range of research topic ideas focused on asset management, probing into the effectiveness of various strategies, the integration of technology, and the alignment with ethical principles among other key dimensions.

• Analysing the effectiveness of different asset allocation strategies in diverse economic environments
• Analysing the methodologies and effectiveness of performance attribution in asset management firms
• Assessing the impact of environmental, social, and governance (ESG) criteria on fund performance
• Examining the role of robo-advisors in modern asset management
• Evaluating how advancements in technology are reshaping portfolio management strategies within asset management firms
• Evaluating the performance persistence of mutual funds and hedge funds
• Investigating the long-term performance of portfolios managed with ethical or socially responsible investing principles
• Investigating the behavioural biases in individual and institutional investment decisions
• Examining the asset allocation strategies employed by pension funds and their impact on long-term fund performance
• Assessing the operational efficiency of asset management firms and its correlation with fund performance

Hedge Funds

Here we explore research topics related to hedge fund operations and strategies, including their implications on corporate governance, financial market stability, and regulatory compliance among other critical facets.

• Assessing the impact of hedge fund activism on corporate governance and financial performance
• Analysing the effectiveness and implications of market-neutral strategies employed by hedge funds
• Investigating how different fee structures impact the performance and investor attraction to hedge funds
• Evaluating the contribution of hedge funds to financial market liquidity and the implications for market stability
• Analysing the risk-return profile of hedge fund strategies during financial crises
• Evaluating the influence of regulatory changes on hedge fund operations and performance
• Examining the level of transparency and disclosure practices in the hedge fund industry and its impact on investor trust and regulatory compliance
• Assessing the contribution of hedge funds to systemic risk in financial markets, and the effectiveness of regulatory measures in mitigating such risks
• Examining the role of hedge funds in financial market stability
• Investigating the determinants of hedge fund success: A comparative analysis

Financial Planning and Advisory

This list explores various research topic ideas related to financial planning, focusing on the effects of financial literacy, the adoption of digital tools, taxation policies, and the role of financial advisors.

• Evaluating the impact of financial literacy on individual financial planning effectiveness
• Analysing how different taxation policies influence financial planning strategies among individuals and businesses
• Evaluating the effectiveness and user adoption of digital tools in modern financial planning practices
• Investigating the adequacy of long-term financial planning strategies in ensuring retirement security
• Assessing the role of financial education in shaping financial planning behaviour among different demographic groups
• Examining the impact of psychological biases on financial planning and decision-making, and strategies to mitigate these biases
• Assessing the behavioural factors influencing financial planning decisions
• Examining the role of financial advisors in managing retirement savings
• A comparative analysis of traditional versus robo-advisory in financial planning
• Investigating the ethics of financial advisory practices

The following list delves into research topics within the insurance sector, touching on the technological transformations, regulatory shifts, and evolving consumer behaviours among other pivotal aspects.

• Analysing the impact of technology adoption on insurance pricing and risk management
• Analysing the influence of Insurtech innovations on the competitive dynamics and consumer choices in insurance markets
• Investigating the factors affecting consumer behaviour in insurance product selection and the role of digital channels in influencing decisions
• Assessing the effect of regulatory changes on insurance product offerings
• Examining the determinants of insurance penetration in emerging markets
• Evaluating the operational efficiency of claims management processes in insurance companies and its impact on customer satisfaction
• Examining the evolution and effectiveness of risk assessment models used in insurance underwriting and their impact on pricing and coverage
• Evaluating the role of insurance in financial stability and economic development
• Investigating the impact of climate change on insurance models and products
• Exploring the challenges and opportunities in underwriting cyber insurance in the face of evolving cyber threats and regulations

Quantitative Finance

These topic ideas span the development of asset pricing models, evaluation of machine learning algorithms, and the exploration of ethical implications among other pivotal areas.

• Developing and testing new quantitative models for asset pricing
• Analysing the effectiveness and limitations of machine learning algorithms in predicting financial market movements
• Assessing the effectiveness of various risk management techniques in quantitative finance
• Evaluating the advancements in portfolio optimisation techniques and their impact on risk-adjusted returns
• Evaluating the impact of high-frequency trading on market efficiency and stability
• Investigating the influence of algorithmic trading strategies on market efficiency and liquidity
• Examining the risk parity approach in asset allocation and its effectiveness in different market conditions
• Examining the application of machine learning and artificial intelligence in quantitative financial analysis
• Investigating the ethical implications of quantitative financial innovations
• Assessing the profitability and market impact of statistical arbitrage strategies considering different market microstructures

Treasury Management

The following topic ideas explore treasury management, focusing on modernisation through technological advancements, the impact on firm liquidity, and the intertwined relationship with corporate governance among other crucial areas.

• Analysing the impact of treasury management practices on firm liquidity and profitability
• Analysing the role of automation in enhancing operational efficiency and strategic decision-making in treasury management
• Evaluating the effectiveness of various cash management strategies in multinational corporations
• Investigating the potential of blockchain technology in streamlining treasury operations and enhancing transparency
• Examining the role of treasury management in mitigating financial risks
• Evaluating the accuracy and effectiveness of various cash flow forecasting techniques employed in treasury management
• Assessing the impact of technological advancements on treasury management operations
• Examining the effectiveness of different foreign exchange risk management strategies employed by treasury managers in multinational corporations
• Assessing the impact of regulatory compliance requirements on the operational and strategic aspects of treasury management
• Investigating the relationship between treasury management and corporate governance

Financial Technology (FinTech)

The following research topic ideas explore the transformative potential of blockchain, the rise of open banking, and the burgeoning landscape of peer-to-peer lending among other focal areas.

• Evaluating the impact of blockchain technology on financial services
• Investigating the implications of open banking on consumer data privacy and financial services competition
• Assessing the role of FinTech in financial inclusion in emerging markets
• Analysing the role of peer-to-peer lending platforms in promoting financial inclusion and their impact on traditional banking systems
• Examining the cybersecurity challenges faced by FinTech firms and the regulatory measures to ensure data protection and financial stability
• Examining the regulatory challenges and opportunities in the FinTech ecosystem
• Assessing the impact of artificial intelligence on the delivery of financial services, customer experience, and operational efficiency within FinTech firms
• Analysing the adoption and impact of cryptocurrencies on traditional financial systems
• Investigating the determinants of success for FinTech startups

Commercial Banking

These topic ideas span commercial banking, encompassing digital transformation, support for small and medium-sized enterprises (SMEs), and the evolving regulatory and competitive landscape among other key themes.

• Assessing the impact of digital transformation on commercial banking services and competitiveness
• Analysing the impact of digital transformation on customer experience and operational efficiency in commercial banking
• Evaluating the role of commercial banks in supporting small and medium-sized enterprises (SMEs)
• Investigating the effectiveness of credit risk management practices and their impact on bank profitability and financial stability
• Examining the relationship between commercial banking practices and financial stability
• Evaluating the implications of open banking frameworks on the competitive landscape and service innovation in commercial banking
• Assessing how regulatory changes affect lending practices and risk appetite of commercial banks
• Examining how commercial banks are adapting their strategies in response to competition from FinTech firms and changing consumer preferences
• Analysing the impact of regulatory compliance on commercial banking operations
• Investigating the determinants of customer satisfaction and loyalty in commercial banking

International Finance

The folowing research topic ideas are centred around international finance and global economic dynamics, delving into aspects like exchange rate fluctuations, international financial regulations, and the role of international financial institutions among other pivotal areas.

• Analysing the determinants of exchange rate fluctuations and their impact on international trade
• Analysing the influence of global trade agreements on international financial flows and foreign direct investments
• Evaluating the effectiveness of international portfolio diversification strategies in mitigating risks and enhancing returns
• Evaluating the role of international financial institutions in global financial stability
• Investigating the role and implications of offshore financial centres on international financial stability and regulatory harmonisation
• Examining the impact of global financial crises on emerging market economies
• Examining the challenges and regulatory frameworks associated with cross-border banking operations
• Assessing the effectiveness of international financial regulations
• Investigating the challenges and opportunities of cross-border mergers and acquisitions

Choosing A Research Topic

These finance-related research topic ideas are starting points to guide your thinking. They are intentionally very broad and open-ended. By engaging with the currently literature in your field of interest, you’ll be able to narrow down your focus to a specific research gap .

When choosing a topic , you’ll need to take into account its originality, relevance, feasibility, and the resources you have at your disposal. Make sure to align your interest and expertise in the subject with your university program’s specific requirements. Always consult your academic advisor to ensure that your chosen topic not only meets the academic criteria but also provides a valuable contribution to the field. 

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50 Best Finance Dissertation Topics For Research Students

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Finance Dissertation Made Easier!

Embarking on your dissertation adventure? Look no further! Choosing the right finance dissertation topics is like laying the foundation for your research journey in Finance, and we're here to light up your path. In this blog, we're diving deep into why dissertation topics in finance matter so much. We've got some golden writing tips to share with you! We're also unveiling the secret recipe for structuring a stellar finance dissertation and exploring intriguing topics across various finance sub-fields. Whether you're captivated by cryptocurrency, risk management strategies, or exploring the wonders of Internet banking, microfinance, retail and commercial banking - our buffet of Finance dissertation topics will surely set your research spirit on fire!

What is a Finance Dissertation?

Finance dissertations are academic papers that delve into specific finance topics chosen by students, covering areas such as stock markets, banking, risk management, and healthcare finance. These dissertations require extensive research to create a compelling report and contribute to the student's confidence and satisfaction in the field of Finance. Now, let's understand why these dissertations are so important and why choosing the right Finance dissertation topics is crucial!

Why Are Finance Dissertation Topics Important?

Choosing the dissertation topics for Finance students is essential as it will influence the course of your research. It determines the direction and scope of your study. You must make sure that the Finance dissertation topics you choose are relevant to your field of interest, or you may end up finding it more challenging to write. Here are a few reasons why finance thesis topics are important:

1. Relevance

Opting for relevant finance thesis topics ensures that your research contributes to the existing body of knowledge and addresses contemporary issues in the field of Finance. Choosing a dissertation topic in Finance that is relevant to the industry can make a meaningful impact and advance understanding in your chosen area.

2. Personal Interest

Selecting Finance dissertation topics that align with your interests and career goals is vital. When genuinely passionate about your research area, you are more likely to stay motivated during the dissertation process. Your interest will drive you to explore the subject thoroughly and produce high-quality work.

3. Future Opportunities

Well-chosen Finance dissertation topics can open doors to various future opportunities. It can enhance your employability by showcasing your expertise in a specific finance area. It may lead to potential research collaborations and invitations to conferences in your field of interest.

4. Academic Supervision

Your choice of topics for dissertation in Finance also influences the availability of academic supervisors with expertise in your chosen area. Selecting a well-defined research area increases the likelihood of finding a supervisor to guide you effectively throughout the dissertation. Their knowledge and guidance will greatly contribute to the success of your research.

Writing Tips for Finance Dissertation

A lot of planning, formatting, and structuring goes into writing a dissertation. It starts with deciding on topics for a dissertation in Finance and conducting tons of research, deciding on methods, and so on. However, you can navigate the process more effectively with proper planning and organisation. Below are some tips to assist you along the way, and here is a blog on the 10 tips on writing a dissertation that can give you more information, should you need it!

1. Select a Manageable Topic

Choosing Finance research topics within the given timeframe and resources is important. Select a research area that interests you and aligns with your career goals. It will help you stay inspired throughout the dissertation process.

2. Conduct a Thorough Literature Review

A comprehensive literature review forms the backbone of your research. After choosing the Finance dissertation topics, dive deep into academic papers, books, and industry reports, gaining a solid understanding of your chosen area to identify research gaps and establish the significance of your study.

3. Define Clear Research Objectives

Clearly define your dissertation's research questions and objectives. It will provide a clear direction for your research and guide your data collection, analysis, and overall structure. Ensure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART).

4. Collect and Analyse Data

Depending on your research methodology and your Finance dissertation topics, collect and analyze relevant data to support your findings. It may involve conducting surveys, interviews, experiments, and analyzing existing datasets. Choose appropriate statistical techniques and qualitative methods to derive meaningful insights from your data.

5. Structure and Organization

Pay attention to the structure and organization of your dissertation. Follow a logical progression of chapters and sections, ensuring that each chapter contributes to the overall coherence of your study. Use headings, subheadings, and clear signposts to guide the reader through your work.

6. Proofread and Edit

Once you have completed the writing process, take the time to proofread and edit your dissertation carefully. Check for clarity, coherence, and proper grammar. Ensure that your arguments are well-supported, and eliminate any inconsistencies or repetitions. Pay attention to formatting, citation styles, and consistency in referencing throughout your dissertation.

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Finance Dissertation Topics

Now that you know what a finance dissertation is and why they are important, it's time to have a look at some of the best Finance dissertation topics. For your convenience, we have segregated these topics into categories, including cryptocurrency, risk management, internet banking, and so many more. So, let's dive right in and explore the best Finance dissertation topics:

Dissertation topics in Finance related to Cryptocurrency

1. The Impact of Regulatory Frameworks on the Volatility and Liquidity of Cryptocurrencies.

2. Exploring the Factors Influencing Cryptocurrency Adoption: A Comparative Study.

3. Assessing the Efficiency and Market Integration of Cryptocurrency Exchanges.

4. An Analysis of the Relationship between Cryptocurrency Prices and Macroeconomic Factors.

5. The Role of Initial Coin Offerings (ICOs) in Financing Startups: Opportunities and Challenges.

Dissertation topics in Finance related to Risk Management

1. The Effectiveness of Different Risk Management Strategies in Mitigating Financial Risks in Banking Institutions.

2. The Role of Derivatives in Hedging Financial Risks: A Comparative Study.

3. Analyzing the Impact of Risk Management Practices on Firm Performance: A Case Study of a Specific Industry.

4. The Use of Stress Testing in Evaluating Systemic Risk: Lessons from the Global Financial Crisis.

5. Assessing the Relationship between Corporate Governance and Risk Management in Financial Institutions.

Dissertation topics in Finance related to Internet Banking

1. Customer Adoption of Internet Banking: An Empirical Study on Factors Influencing Usage.

Enhancing Security in Internet Banking: Exploring Biometric Authentication Technologies.

2. The Impact of Mobile Banking Applications on Customer Engagement and Satisfaction.

3. Evaluating the Efficiency and Effectiveness of Internet Banking Services in Emerging Markets.

4. The Role of Social Media in Shaping Customer Perception and Adoption of Internet Banking.

Dissertation topics in Finance related to Microfinance

1. The Impact of Microfinance on Poverty Alleviation: A Comparative Study of Different Models.

2. Exploring the Role of Microfinance in Empowering Women Entrepreneurs.

3. Assessing the Financial Sustainability of Microfinance Institutions in Developing Countries.

4. The Effectiveness of Microfinance in Promoting Rural Development: Evidence from a Specific Region.

5. Analyzing the Relationship between Microfinance and Entrepreneurial Success: A Longitudinal Study.

Dissertation topics in Finance related to Retail and Commercial Banking

1. The Impact of Digital Transformation on Retail and Commercial Banking: A Case Study of a Specific Bank.

2. Customer Satisfaction and Loyalty in Retail Banking: An Analysis of Service Quality Dimensions.

3. Analyzing the Relationship between Bank Branch Expansion and Financial Performance.

4. The Role of Fintech Startups in Disrupting Retail and Commercial Banking: Opportunities and Challenges.

5. Assessing the Impact of Mergers and Acquisitions on the Performance of Retail and Commercial Banks.

Dissertation topics in Finance related to Alternative Investment

1. The Performance and Risk Characteristics of Hedge Funds: A Comparative Analysis.

2. Exploring the Role of Private Equity in Financing and Growing Small and Medium-Sized Enterprises.

3. Analyzing the Relationship between Real Estate Investments and Portfolio Diversification.

4. The Potential of Impact Investing: Evaluating the Social and Financial Returns.

5. Assessing the Risk-Return Tradeoff in Cryptocurrency Investments: A Comparative Study.

Dissertation topics in Finance related to International Affairs

1. The Impact of Exchange Rate Volatility on International Trade: A Case Study of a Specific Industry.

2. Analyzing the Effectiveness of Capital Controls in Managing Financial Crises: Comparative Study of Different Countries.

3. The Role of International Financial Institutions in Promoting Economic Development in Developing Countries.

4. Evaluating the Implications of Trade Wars on Global Financial Markets.

5. Assessing the Role of Central Banks in Managing Financial Stability in a Globalized Economy.

Dissertation topics in Finance related to Sustainable Finance

1. The impact of sustainable investing on financial performance.

2. The role of green bonds in financing climate change mitigation and adaptation.

3. The development of carbon markets.

4. The use of environmental, social, and governance (ESG) factors in investment decision-making.

5. The challenges and opportunities of sustainable Finance in emerging markets.

Dissertation topics in Finance related to Investment Banking

1. The valuation of distressed assets.

2. The pricing of derivatives.

3. The risk management of financial institutions.

4. The regulation of investment banks.

5. The impact of technology on the investment banking industry.

Dissertation topics in Finance related to Actuarial Science

1. The development of new actuarial models for pricing insurance products.

2. The use of big data in actuarial analysis.

3. The impact of climate change on insurance risk.

4. The design of pension plans that are sustainable in the long term.

5. The use of actuarial science to manage risk in other industries, such as healthcare and Finance.

Tips To Find Good Finance Dissertation Topics 

Embarking on a financial dissertation journey requires careful consideration of various factors. Your choice of topic in finance research topics is pivotal, as it sets the stage for the entire research process. Finding a good financial dissertation topic is essential to blend your interests with the current trends in the financial landscape. We suggest the following tips that can help you pick the perfect dissertation topic:

1. Identify your interests and strengths 

2. Check for current relevance

3. Feedback from your superiors

4. Finalise the research methods

5. Gather the data

6. Work on the outline of your dissertation

7. Make a draft and proofread it

In this blog, we have discussed the importance of finance thesis topics and provided valuable writing tips and tips for finding the right topic, too. We have also presented a list of topics within various subfields of Finance. With this, we hope you have great ideas for finance dissertations. Good luck with your finance research journey!

Frequently Asked Questions

How do i research for my dissertation project topics in finance, what is the best topic for dissertation topics for mba finance, what is the hardest finance topic, how do i choose the right topic for my dissertation in finance, where can i find a dissertation topic in finance.

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Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis

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• Published: 20 January 2024
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Over the past two decades, artificial intelligence (AI) has experienced rapid development and is being used in a wide range of sectors and activities, including finance. In the meantime, a growing and heterogeneous strand of literature has explored the use of AI in finance. The aim of this study is to provide a comprehensive overview of the existing research on this topic and to identify which research directions need further investigation. Accordingly, using the tools of bibliometric analysis and content analysis, we examined a large number of articles published between 1992 and March 2021. We find that the literature on this topic has expanded considerably since the beginning of the XXI century, covering a variety of countries and different AI applications in finance, amongst which Predictive/forecasting systems, Classification/detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Furthermore, we show that the selected articles fall into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk and default evaluation, cryptocurrencies, derivatives, credit risk in banks, investor sentiment analysis and foreign exchange management, respectively. Future research should seek to address the partially unanswered research questions and improve our understanding of the impact of recent disruptive technological developments on finance.

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Introduction

The first two decades of the twenty-first century have experienced an unprecedented way of technological progress, which has been driven by advances in the development of cutting-edge digital technologies and applications in Artificial Intelligence (AI). Artificial intelligence is a field of computer science that creates intelligent machines capable of performing cognitive tasks, such as reasoning, learning, taking action and speech recognition, which have been traditionally regarded as human tasks (Frankenfield 2021 ). AI comprises a broad and rapidly growing number of technologies and fields, and is often regarded as a general-purpose technology, namely a technology that becomes pervasive, improves over time and generates complementary innovation (Bresnahan and Trajtenberg 1995 ). As a result, it is not surprising that there is no consensus on the way AI is defined (Van Roy et al. 2020 ). An exhaustive definition has been recently proposed by Acemoglu and Restrepo ( 2020 , p.1), who assert that Artificial Intelligence is “(…) the study and development of intelligent (machine) agents, which are machines, software or algorithms that act intelligently by recognising and responding to their environment.” Even though it is often difficult to draw precise boundaries, this promising and rapidly evolving field mainly comprises machine learning, deep learning, NLP (natural language processing) platforms, predictive APIs (application programming interface), image recognition and speech recognition (Martinelli et al. 2021 ).

The term “Artificial intelligence” was first coined by John McCarthy in 1956 during a conference at Dartmouth College to describe “thinking machines” (Buchanan 2019 ). However, until 2000, the lack of storage capability and low computing power prevented any progress in the field. Accordingly, governments and investors lost their interest and AI fell short of financial support and funding in 1974–1980 and again in 1987–1993. These periods of funding shortage are also known as “AI winters Footnote 1 ”.

However, the most significant development and spread of AI-related technologies is much more recent, and has been prompted by the availability of large unstructured databases, the explosion of computing power, and the rise in venture capital intended to support innovative, technological projects (Ernst et al. 2018 ). One of the most distinctive The term AI winter first appeared in 1characteristics of AI technologies is that, unlike industrial robots, which need to receive specific instructions, generally provided by a software, before they perform any action, can learn for themselves how to map information about the environment, such as visual and tactile data from a robot’s sensors, into instructions sent to the robot’s actuators (Raj and Seamans 2019 ). Additionally, as remarked by Ernst et al. ( 2018 ), whilst industrial robots mostly perform manual tasks, AI technologies are able to carry out activities that, until some years ago, were still regarded as typically human, i.e. what Ernst and co-authors label as “mental tasks”.

The adoption of AI is likely to have remarkable implications for the subjects adopting them and, more in general, for the economy and the society. In particular, it is expected to contribute to the growth of the global GDP, which, according to a study conducted by Pricewater-house-Coopers (PwC) and published in 2017, is likely to increase by up to 14% by 2030. Moreover, companies adopting AI technologies sometimes report better performance (Van Roy et al. 2020 ). Concerning the geographic dimension of this field, North America and China are the leading investors and are expected to benefit the most from AI-driven economic returns. Europe and emerging markets in Asia and South America will follow, with moderate profits owing to fewer and later investments (PwC 2017 ). AI is going to affect labour markets as well. The demand for high-skilled employees is expected to increase, whilst the demand for low-skilled jobs is likely to shrink because of automation; the resulting higher unemployment rate, however, is going to be offset by the new job opportunities offered by AI (Ernst et al. 2018 ; Acemoglu and Restrepo 2020 ).

AI solutions have been introduced in every major sector of the economy; a sector that is witnessing a profound transformation led by the ongoing technological revolution is the financial one. Financial institutions, which rely heavily on Big Data and process automation, are indeed in a “unique position to lead the adoption of AI” (PwC 2020 ), which generates several benefits: for instance, it encourages automation of manufacturing processes which in turn enhances efficiency and productivity. Next, since machines are immune to human errors and psychological factors, it ensures accurate and unbiased predictive analytics and trading strategies. AI also fosters business model innovation and radically changes customer relationships by promoting customised digital finance, which, together with the automation of processes, results in better service efficiency and cost-saving (Cucculelli and Recanatini 2022 ). Furthermore, AI is likely to have substantial implications for financial conduct and prudential supervisors, and it also has the potential to help supervisors identify potential violations and help regulators better anticipate the impact of changes in regulation (Wall 2018 ). Additionally, complex AI/machine learning algorithms allow Fintech lenders to make fast (almost instantaneous) credit decisions, with benefits for both the lenders and the consumers (Jagtiani and John 2018 ). Intelligent devices in Finance are used in a number of areas and activities, including fraud detection, algorithmic trading and high-frequency trading, portfolio management, credit decisions based on credit scoring or credit approval models, bankruptcy prediction, risk management, behavioural analyses through sentiment analysis and regulatory compliance.

In recent years, the adoption of AI technologies in a broad range of financial applications has received increasing attention by scholars; however, the extant literature, which is reviewed in the next section, is quite broad and heterogeneous in terms of research questions, country and industry under scrutiny, level of analysis and method, making it difficult to draw robust conclusions and to understand which research areas require further investigation. In the light of these considerations, we conduct an extensive review of the research on the use of AI in Finance thorough which we aim to provide a comprehensive account of the current state of the art and, importantly, to identify a number of research questions that are still (partly) unanswered. This survey may serve as a useful roadmap for researchers who are not experts of this topic and could find it challenging to navigate the extensive and composite research on this subject. In particular, it may represent a useful starting point for future empirical contributions, as it provides an account of the state of the art and of the issues that deserve further investigation. In doing so, this study complements some previous systematic reviews on the topic, such as the ones recently conducted by Hentzen et al. ( 2022b ) and (Biju et al. 2020 ), which differ from our work in the following main respects: Hentzen and co-authors’ study focuses on customer-facing financial services, whilst the valuable contribution of Biju et al. poses particular attention to relevant technical aspects and the assessment of the effectiveness and the predictive capability of machine learning, AI and deep learning mechanisms within the financial sphere; in doing so, it covers an important issue which, however, is out of the scope of our work.

From our review, it emerges that, from the beginning of the XXI century, the literature on this topic has significantly expanded, and has covered a broad variety of countries, as well as several AI applications in finance, amongst which Predictive/forecasting systems, Classification /detection/early warning systems and Big data Analytics/Data mining /Text mining stand out. Additionally, we show that the selected articles can be grouped into ten main research streams, in which AI is applied to the stock market, trading models, volatility forecasting, portfolio management, performance, risk & default evaluation, cryptocurrencies, derivatives, credit risks in banks, investor sentiment analysis and foreign exchange management, respectively.

The balance of this paper is organised as follows: Sect. “ Methodology ” shortly presents the methodology. Sect. “ A detailed account of the literature on AI in Finance ” illustrates the main results of the bibliometric analysis and the content analysis. Sect. “ Issues that deserve further investigation ” draws upon the research streams described in the previous section to pinpoint several potential research avenues. Sect. “ Conclusions ” concludes. Finally, Appendix 1 clarifies some AI-related terms and definitions that appear several times throughout the paper, whilst Appendix 2 provides more information on some of the articles under scrutiny.

Methodology

To conduct a sound review of the literature on the selected topic, we resort to two well-known and extensively used approaches, namely bibliometric analysis and content analysis. Bibliometric analysis is a popular and rigorous method for exploring and analysing large volumes of scientific data which allows us to unpack the evolutionary nuances of a specific field whilst shedding light on the emerging areas in that field (Donthu et al. 2021 ). In this study, we perform bibliometric analysis using HistCite, a popular software package developed to support researchers in elaborating and visualising the results of literature searches in the Web of Science platform. Specifically, we employ HistCite to recover the annual number of publications, the number of forward citations (which we use to identify the most influential journals and articles) and the network of co-citations, namely, all the citations received and given by journals belonging to a certain field, which help us identify the major research streams described in Sect. “ Identification of the major research streams ”. After that, to delve into the contents of the most pertinent studies on AI in finance, we resort to traditional content analysis, a research method that provides a systematic and objective means to make valid inferences from verbal, visual, or written data which, in turn, permit to describe and quantify specific phenomena (Downe-Wambolt 1992 ).

In order to identify the sample of studies on which bibliometric and content analysis were performed, we proceeded as follows. First, we searched for pertinent articles published in English be-tween 1950 and March 2021. Specifically, we scrutinised the “Finance”, “Economics”, “Business Finance” and “Business” sections of the “Web of Science” (WoS) database using the keyword “finance” together with an array of keywords concerning Artificial Intelligence (i.e. “Finance” AND (“Artificial Intelligence” OR “Machine Learning” OR “Deep Learning” OR “Neural Networks*” OR “Natural Language Processing*” OR “Algorithmic Trading*” OR “Artificial Neural Network” OR “Robot*” OR “Automation” OR “Text Mining” OR “Data Mining” OR “Soft Computing” OR “Fuzzy Logic Analysis” OR “Biometrics*” OR “Geotagging” OR “Wearable*” OR “IoT” OR “Internet of Thing*” OR “digitalization” OR “Artificial Neutral Networks” OR “Big Data” OR “Industry 4.0″ OR “Smart products*” OR Cloud Computing” OR “Digital Technologies*”). In doing so, we ended up with 1,218 articles. Next, two researchers independently analysed the title, abstract and content of these papers and kept only those that address the topic under scrutiny in a non-marginal and non-trivial way. This second step reduced the number of eligible papers to 892, which were used to perform the first part of the bibliometric analysis. Finally, we delved into the contents of the previously selected articles and identified 110 contributions which specifically address the adoption and implications in Finance of AI tools focussing on the economic dimension of the topic, and which are employed in the second part of the bibliometric analysis and in the content analysis.

A detailed account of the literature on AI in Finance

In this section, we explore the patterns and trends in the literature on AI in Finance in order to obtain a compact but exhaustive account of the state of the art. Specifically, we identify some relevant bibliographic characteristics using the tools of bibliometric analysis. After that, focussing on a sub-sample of papers, we conduct a preliminary assessment of the selected studies through a content analysis and detect the main AI applications in Finance. Finally, we identify and briefly describe ten major research streams.

Main results of the bibliometric analysis

First, using HistCite and considering the sample of 892 studies, we computed, for each year, the number of publications related to the topic “AI in Finance”. The corresponding publication trend is shown in Fig.  1 , which plots both the annual absolute number of sampled papers (bar graph in blue) and the ratio between the latter and the annual overall amount of publications (indexed in Scopus) in the finance area (line graph in orange). We also compute relative numbers to see if the trend emerging from the selected studies is not significantly attributable to a “common trend” (i.e. to the fact that, in the meantime, also the total number of publications in the financial area has significantly increased). It can be noted that both graphs exhibit a strong upward trend from 2015 onwards; during the most recent years, the pace of growth and the degree of pervasiveness of AI adoption in the financial sphere have indeed remarkably strengthened, and have become the subject of a rapidly growing number of research articles.

Publication Trend, 1992–2021

After that, focussing on the more pertinent (110) articles, we checked the journals in which these studies were published. Table 1 presents the top-ten list of journals reported in the Academic Journal Guide-ABS List 2020 and ranked on the basis of the total global citation score (TGCS), which captures the number of times an article is cited by other articles that deal with the same topic and are indexed in the WoS database. For each journal, we also report the total number of studies published in that journal. We can notice that the most influential journals in terms of TGCS are the Journal of Finance (with a TGCS equal to 1283) and the Journal of Banking and Finance (with a TGCS of 1253), whilst the journals containing the highest number of articles on the topic are Quantitative Finance (68 articles) and Intelligent Systems in Accounting, Finance and Management (43).

Finally, Fig.  2 provides a visual representation of the citation-based relationships amongst papers starting from the most-cited papers, which we obtained using the Java application CiteSpace.

Source: authors’ elaboration of data from Web of Science; visualisation produced using CiteSpace

Citation Mapping and identification of the research streams.

Preliminary results of the content analysis

In this paragraph, we shortly illustrate some relevant characteristics of our sub-sample made up of 110 studies, including country and industry coverage, method and underpinning theoretical background. Table 2 comprises the list of countries under scrutiny, and, for each of them, a list of papers that perform their analysis on that country. We can see that our sample exhibits significant geographical heterogeneity, as it covers 74 countries across all continents; however, the most investigated areas are three, that is Europe, the US and China. These results corroborate the fact that the above-mentioned regions are the leaders of the AI-driven financial industry, as suggested by PwC ( 2017 ). The United States, in particular, are considered the “early adopters” of AI and are likely to benefit the most from this source of competitive advantage. More lately, emerging countries in Southeast Asia and the Middle East have received growing interest. Finally, a smaller number of papers address underdeveloped regions in Africa and various economies in South America.

The most investigated sectors are reported in Table  3 . We can notice that, although it primarily deals with banking and financial services, the extant research has addressed the topic in a vast array of industries. This confirms that the application potential of AI is very broad, and that any industry may benefit from it.

Through our analysis, we also detected the key theories and frameworks applied by researchers in the prior literature. As shown in Table  4 , 73 (out of 110) papers explicitly refer to some theoretical framework. Specifically, ten of them (14%) resort to computational learning theory; this theory, which is an extension of statistical learning, provides researchers with a theoretical guide for finding the most suitable learning model for a given problem, and is regarded as one of the most important and most used theories in the field. Specific theories concerning types of neural networks and learning methods are used too, such as the fuzzy set theory, which is mentioned in 8% of the sample, and to a lesser extent, the Naive Bayes theorem, the theory of neural networks, the theory of genetic programming and the TOPSIS analytical framework. Finance theories (e.g. Arbitrage Pricing Theory; Black and Scholes 1973 ) are jointly employed with portfolio management theories (e.g. modern portfolio theory), and the two of them account together for 21% (15) of the total number of papers. Finally, bankruptcy theories support business failure forecasts, whilst other theoretical underpinnings concern mathematical and probability concepts.

The content analysis also provides information on the main types of companies under scrutiny. Table 5 indicates that 30 articles (out of 110) focus on large companies listed on stock exchanges, whilst only 16 studies cover small and medium enterprises. Similarly, trading and digital platforms are examined in 16 papers that deal with derivatives and cryptocurrencies.

Furthermore, Table  6 summarises the key methods applied in the literature, which are divided by category (note that all the papers employ more than one method). Looking at the table, we see that machine learning and artificial neural networks are the most popular ones (they are employed in 41 and 51 articles, respectively). The majority of the papers resort to different approaches to compare their results with those obtained through autoregressive and regression models or conventional statistics, which are used as the benchmark; therefore, there may be some overlaps. Nevertheless, we notice that support vector machine and random forest are the most widespread machine learning methods. On the other hand, the use of artificial neural networks (ANNs) is highly fragmented. Backpropagation, Recurrent, and Feed-Forward NNs are considered basic neural nets and are commonly employed. Advanced NNs, such as Higher-Order Neural network (HONN) and Long Short-Term Memory Networks (LSTM), are more performing than their standard version but also much more complicated to apply. These methods are usually compared to autoregressive models and regressions, such as ARMA, ARIMA, and GARCH. Finally, we observe that almost all the sampled papers are quantitative, whilst only three of them are qualitative and four of them consist in literature reviews.

A taxonomy of AI applications in Finance

After scrutinising some relevant features of the papers, we make a step forward and outline a taxonomy of AI applications used in Finance and tackled by previous literature. The main uses of AI in Finance and the papers that address each of them are summarised in Table  7 .

Many research papers (39 out of 110) employ AI as a predictive instrument for forecasting stock prices, performance and volatility. In 23 papers, AI is employed in classification problems and warning systems to detect credit risk and frauds, as well as to monitor firm or bank performance. The former use of AI permits to classify firms into two categories based on qualitative and quantitative data; for example, we may have distressed or non-distressed, viable–nonviable, bankrupt–non-bankrupt, or financially healthy–not healthy, good–bad, and fraud–not fraud. Warning systems follow a similar principle: after analysing customers’ financial behaviour and classifying potential fraud issues in bank accounts, alert models signal to the bank unusual transactions. Additionally, we see that 14 articles employ text mining and data mining language recognition, i.e. natural language processing, as well as sentiment analysis. This may be the starting point of AI-driven behavioural analysis in Finance. Amongst others, trading models and algorithmic trading are further popular aspects of AI widely analysed in the literature. Moreover, interest in Robo-advisory is growing in the asset investment field. Finally, less studied AI applications concern the modelling capability of algorithms and traditional machine learning and neural networks.

Identification of the major research streams

Drawing upon the co-citation analysis mentioned in Sect. " Methodology ", we detected ten main research streams: (1) AI and the stock market; (2) AI and Trading Models; (3) AI and Volatility Forecasting; (4) AI and Portfolio Management; (5) AI and Performance, Risk, and Default Valuation; (6) AI and Bitcoin, Cryptocurrencies; (7) AI and Derivatives; (8) AI and Credit Risk in Banks; (9) AI and Investor Sentiments Analysis; (10) AI and Foreign Exchange Management. Some research streams can be further divided into sub-streams as they deal with various aspects of the same main topic. In this section, we provide a compact account for each of the aforementioned research streams. More detailed information on some of the papers fuelling them is provided in Appendix 2.

Stream 01: AI and the stock market

The stream “AI and the Stock Market” comprises two sub-streams, namely algorithmic trading and stock market, and AI and stock price prediction. The first sub-stream deals with the impact of algorithmic trading (AT) on financial markets. In this regard, Herdershott et al. ( 2011 ) argue that AT increases market liquidity by reducing spreads, adverse selection, and trade-related price discovery. This results in a lowered cost of equity for listed firms in the medium–long term, especially in emerging markets (Litzenberger et al. 2012 ). As opposed to human traders, algorithmic trading adjusts faster to information and generates higher profits around news announcements thanks to better market timing ability and rapid executions (Frino et al. 2017 ). Even though high-frequency trading (a subset of algorithmic trading) has sometimes increased volatility related to news or fundamentals, and transmitted it within and across industries, AT has overall reduced return volatility variance and improved market efficiency (Kelejian and Mukerji 2016 ; Litzenberger et al. 2012 ).

The second sub-stream investigates the use of neural networks and traditional methods to forecast stock prices and asset performance. ANNs are preferred to linear models because they capture the non-linear relationships between stock returns and fundamentals and are more sensitive to changes in variables relationships (Kanas 2001 ; Qi 1999 ). Dixon et al. ( 2017 ) argue that deep neural networks have strong predictive power, with an accuracy rate equal to 68%. Also, Zhang et al. ( 2021 ) propose a model, the Long Short-Term Memory Networks (LSTM), that outperforms all classical ANNs in terms of prediction accuracy and rational time cost, especially when various proxies of online investor attention (such as the internet search volume) are considered.

Stream 02: AI and trading models

From the review of the literature represented by this stream, it emerges that neural networks and machine learning algorithms are used to build intelligent automated trading systems. To give some examples, Creamer and Freund ( 2010 ) create a machine learning-based model that analyses stock price series and then selects the best-performing assets by suggesting a short or long position. The model is also equipped with a risk management overlayer preventing the transaction when the trading strategy is not profitable. Similarly, Creamer ( 2012 ) uses the above-mentioned logic in high-frequency trading futures: the model selects the most profitable and less risky futures by sending a long or short recommendation. To construct an efficient trading model, Trippi and DeSieno ( 1992 ) combine several neural networks into a single decision rule system that outperforms the single neural networks; Kercheval and Zhang ( 2015 ) use a supervised learning method (i.e. multi-class SVM) that automatically predicts mid-price movements in high-frequency limit order books by classifying them in low-stationary-up; these predictions are embedded in trading strategies and yield positive payoffs with controlled risk.

Stream 03: AI and volatility forecasting

The third stream deals with AI and the forecasting of volatility. The volatility index (VIX) from Chicago Board Options Exchange (CBOE) is a measure of market sentiment and expectations. Forecasting volatility is not a simple task because of its very persistent nature (Fernandes et al. 2014 ). According to Fernandes and co-authors, the VIX is negatively related to the SandP500 index return and positively related to its volume. The heterogeneous autoregressive (HAR) model yields the best predictive results as opposed to classical neural networks (Fernandes et al. 2014 ; Vortelinos 2017 ). Modern neural networks, such as LSTM and NARX (nonlinear autoregressive exogenous network), also qualify as valid alternatives (Bucci 2020 ). Another promising class of neural networks is the higher-order neural network (HONN) used to forecast the 21-day-ahead realised volatility of FTSE100 futures. Thanks to its ability to capture higher-order correlations within the dataset, HONN shows remarkable performance in terms of statistical accuracy and trading efficiency over multi-layer perceptron (MLP) and the recurrent neural network (RNN) (Sermpinis et al. 2013 ).

Stream 04: AI and portfolio management

This research stream analyses the use of AI in portfolio selection. As an illustration, Soleymani and Vasighi ( 2020 ) consider a clustering approach paired with VaR analysis to improve asset allocation: they group the least risky and more profitable stocks and allocate them in the portfolio. More elaborate asset allocation designs incorporate a bankruptcy detection model and an advanced utility performance system: before adding the stock to the portfolio, the sophisticated neural network estimates the default probability of the company and asset’s contribution to the optimal portfolio (Loukeris and Eleftheriadis 2015 ). Index-tracking powered by deep learning technology minimises tracking error and generates positive performance (Kim and Kim 2020 ). The asymmetric copula method for returns dependence estimates further promotes the portfolio optimization process (Zhao et al. 2018 ). To sum up, all papers show that AI-based prediction models improve the portfolio selection process by accurately forecasting stock returns (Zhao et al. 2018 ).

Stream 05: AI and performance, risk, default valuation

This research stream comprises three sub-streams, namely AI and Corporate Performance, Risk and Default Valuation; AI and Real Estate Investment Performance, Risk, and Default Valuation; AI and Banks Performance, Risk and Default Valuation.

The first sub-stream examines corporate financial conditions to predict financially distressed companies (Altman et al. 1994 ). As an illustration, Jones et al. ( 2017 ) and Gepp et al. ( 2010 ) determine the probability of corporate default. Sabău Popa et al. ( 2021 ) predict business performance based on a composite financial index. The findings of the aforementioned papers confirm that AI-powered classifiers are extremely accurate and easy to interpret, hence, superior to classic linear models. A quite interesting paper surveys the relationship between face masculinity traits in CEOs and firm riskiness through image processing (Kamiya et al. 2018 ). The results reveal that firms lead by masculine-faced CEO have higher risk and leverage ratios and are more frequent acquirers in MandA operations.

The second sub-stream focuses on mortgage and loan default prediction (Feldman and Gross 2005 ; Episcopos, Pericli, and Hu, 1998 ). For instance, Chen et al. ( 2013 ) evaluate real estate investment returns by forecasting the REIT index; they show that the industrial production index, the lending rate, the dividend yield and the stock index influence real estate investments. All the forecasting techniques adopted (i.e. supervised machine learning and ANNs) outperform linear models in terms of efficiency and precision.

The third sub-stream deals with banks’ performance. In contradiction with past research, a text mining study argues that the most important risk factors in banking are non-financial, i.e. regulation, strategy and management operation. However, the findings from text analysis are limited to what is disclosed in the papers (Wei et al. 2019 ). A highly performing NN-based study on the Malaysian and Islamic banking sector asserts that negative cost structure, cultural aspects and regulatory barriers (i.e. low competition) lead to inefficient banks compared to the U.S., which, on the contrary, are more resilient, healthier and well regulated (Wanke et al. 2016a, b, c, d; Papadimitriou et al. 2020 ).

Stream 06: AI and cryptocurrencies

Although algorithms and AI advisors are gaining ground, human traders still dominate the cryptocurrency market (Petukhina et al. 2021 ). For this reason, substantial arbitrage opportunities are available in the Bitcoin market, especially for USD–CNY and EUR–CNY currency pairs (Pichl and Kaizoji 2017 ). Concerning daily realised volatility, the HAR model delivers good results. Likewise, the feed-forward neural network effectively approximates the daily logarithmic returns of BTCUSD and the shape of their distribution (Pichl and Kaizoji 2017 ).

Additionally, the Hierarchical Risk Parity (HRP) approach, an asset allocation method based on machine learning, represents a powerful risk management tool able to manage the high volatility characterising Bitcoin prices, thereby helping cryptocurrency investors (Burggraf 2021 ).

Stream 07: AI and derivatives

ANNs and machine learning models are accurate predictors in pricing financial derivatives. Jang and Lee ( 2019 ) propose a machine learning model that outperforms traditional American option pricing models: the generative Bayesian NN; Culkin and Das ( 2017 ) use a feed-forward deep NN to reproduce Black and Scholes’ option pricing formula with a high accuracy rate. Similarly, Chen and Wan ( 2021 ) suggest a deep NN for American option and deltas pricing in high dimensions. Funahashi ( 2020 ), on the contrary, rejects deep learning for option pricing due to the instability of the prices, and introduces a new hybrid method that combines ANNs and asymptotic expansion (AE). This model does not directly predict the option price but measures instead, the difference between the target (i.e. derivative price) and its approximation. As a result, the ANN becomes faster, more accurate and “lighter” in terms of layers and training data volume. This innovative method mimics a human learning process when one learns about a new object by recognising its differences from a similar and familiar item (Funahashi 2020 ).

Stream 08: AI and credit risk in banks

The research stream labelled “AI and Credit Risk in Banks” Footnote 2 includes the following sub-streams: AI and Bank Credit Risk; AI and Consumer Credit Risk and Default; AI and Financial Fraud detection/ Early Warning System; AI and Credit Scoring Models.

The first sub-stream addresses bank failure prediction. Machine learning and ANNs significantly outperform statistical approaches, although they lack transparency (Le and Viviani 2018 ). To overcome this limitation, Durango‐Gutiérrez et al. ( 2021 ) combine traditional methods (i.e. logistic regression) with AI (i.e. Multiple layer perceptron -MLP), thus gaining valuable insights on explanatory variables. With the scope of preventing further global financial crises, the banking industry relies on financial decision support systems (FDSSs), which are strongly improved by AI-based models (Abedin et al. 2019 ).

The second sub-stream compares classic and advanced consumer credit risk models. Supervised learning tools, such as SVM, random forest, and advanced decision trees architectures, are powerful predictors of credit card delinquency: some of them can predict credit events up to 12 months in advance (Lahmiri 2016 ; Khandani et al. 2010 ; Butaru et al. 2016 ). Jagric et al. ( 2011 ) propose a learning vector quantization (LVQ) NN that better deals with categorical variables, achieving an excellent classification rate (i.e. default, non-default). Such methods overcome logit-based approaches and result in cost savings ranging from 6% up to 25% of total losses (Khadani et al. 2010 ).

The third group discusses the role of AI in early warning systems. On a retail level, advanced random forests accurately detect credit card fraud based on customer financial behaviour and spending pattern, and then flag it for investigation (Kumar et al. 2019 ). Similarly, Coats and Fant ( 1993 ) build a NN alert model for distressed firms that outperforms linear techniques. On a macroeconomic level, systemic risk monitoring models enhanced by AI technologies, i.e. k-nearest neighbours and sophisticated NNs, support macroprudential strategies and send alerts in case of global unusual financial activities (Holopainen, and Sarlin 2017 ; Huang and Guo 2021 ). However, these methods are still work-in-progress.

The last group studies intelligent credit scoring models, with machine learning systems, Adaboost and random forest delivering the best forecasts for credit rating changes. These models are robust to outliers, missing values and overfitting, and require minimal data intervention (Jones et al. 2015 ). As an illustration, combining data mining and machine learning, Xu et al. ( 2019 ) build a highly sophisticated model that selects the most important predictors and eliminates noisy variables, before performing the task.

Stream 09: AI and investor sentiment analysis

Investor sentiment has become increasingly important in stock prediction. For this purpose, sentiment analysis extracts investor sentiment from social media platforms (e.g. StockTwits, Yahoo-finance, eastmoney.com) through natural language processing and data mining techniques, and classifies it into negative or positive (Yin et al. 2020 ). The resulting sentiment is regarded either as a risk factor in asset pricing models, an input to forecast asset price direction, or an intraday stock index return (Houlihan and Creamer 2021 ; Renault 2017 ). In this respect, Yin et al. ( 2020 ) find that investor sentiment has a positive correlation with stock liquidity, especially in slowing markets; additionally, sensitivity to liquidity conditions tends to be higher for firms with larger size and a higher book-to-market ratio, and especially those operating in weakly regulated markets. As for predictions, daily news usually predicts stock returns for few days, whereas weekly news predicts returns for longer period, from one month to one quarter. This generates a return effect on stock prices, as much of the delayed response to news occurs around major events in company life, specifically earnings announcement, thus making investor sentiment a very important variable in assessing the impact of AI in financial markets. (Heston and Sinha 2017 ).

Stream 10: AI and foreign exchange management

The last stream addresses AI and the management of foreign exchange. Cost-effective trading or hedging activities in this market require accurate exchange rate forecasts (Galeshchuk and Mukherjee 2017 ). In this regard, the HONN model significantly outperforms traditional neural networks (i.e. multi-layer perceptron, recurrent NNs, Psi sigma-models) in forecasting and trading the EUR/USD currency pair using ECB daily fixing series as input data (Dunis et al. 2010 ). On the contrary, Galeshchuk and Mukherjee ( 2017 ) consider these methods as unable to predict the direction of change in the forex rates and, therefore, ineffective at supporting profitable trading. For this reason, they apply a deep NN (Convolution NNs) to forecast three main exchange rates (i.e. EUR/USD, GBP/USD, and JPY/USD). The model performs remarkably better than time series models (e.g. ARIMA: Autoregressive integrated moving average) and machine learning classifiers. To sum up, from this research stream it emerges that AI-based models, such as NARX and the above-mentioned techniques, achieve better prediction performance than statistical or time series models, as remarked by Amelot et al. ( 2021 ).

Issues that deserve further investigation

As shown in Sect. " A detailed account of the literature on AI in Finance ", the literature on Artificial Intelligence in Finance is vast and rapidly growing as technological progress advances. There are, however, some aspects of this subject that are unexplored yet or that require further investigation. In this section, we further scrutinise, through content analysis, the papers published between 2015 and 2021 (as we want to focus on the most recent research directions) in order to define a potential research agenda. Hence, for each of the ten research streams presented in Sect. " Identification of the major research streams ", we report a number of research questions that were put forward over time and are still at least partly unaddressed. The complete list of research questions is enclosed in Table  8 .

AI and the stock market

This research stream focuses on algorithmic trading (AT) and stock price prediction. Future research in the field could analyse more deeply alternative AI-based market predictors (e.g. clustering algorithms and similar learning methods) and draw up a regime clustering algorithm in order to get a clearer view of the potential applications and benefits of clustering methodologies (Law, and Shawe-Taylor 2017 ). In this regard, Litzenberger et al. ( 2012 ) and Booth et al. ( 2015 ) recommend broadening the study to market cycles and regulation policies that may affect AI models’ performance in stock prediction and algorithmic trading, respectively. Footnote 3 Furthermore, forecasting models should be evaluated with deeper order book information, which may lead to a higher prediction accuracy of stock prices (Tashiro et al. 2019 ).

AI and trading models

This research stream builds on the application of AI in trading models. Robo advisors are the evolution of basic trading models: they are easily accessible, cost-effective, profitable for investors and, unlike human traders, immune to behavioural biases. Robo advisory, however, is a recent phenomenon and needs further performance evaluations, especially in periods of financial distress, such as the post-COVID-19 one (Tao et al. 2021 ), or in the case of the so-called “Black swan” events. Conversely, trading models based on spatial neural networks (an advanced ANN) outperform all statistical techniques in modelling limit order books and suggest an extensive interpretation of the joint distribution of the best bid and best ask. Given the versatility of such a method, forthcoming research should resort to it with the aim of understanding whether neural networks with more order book information (i.e. order flow history) lead to better trading performance (Sirignano 2018 ).

AI and volatility forecasting

As previously mentioned, volatility forecasting is a challenging task. Although recent studies report solid results in the field (see Sermpinis et al. 2013 ; Vortelinos 2017 ), future work could deploy more elaborated recurrent NNs by modifying the activation function of the processing units composing the ANNs, or by adding hidden layers and then evaluate their performance (Bucci 2020 ). Since univariate time series are commonly used for realised volatility prediction, it would be interesting to also inquire about the performance of multivariate time series.

AI and portfolio management

This research stream examines the use of AI in portfolio selection strategies. Past studies have developed AI models that are capable of replicating the performance of stock indexes (known as index tracking strategy) and constructing efficient portfolios with no human intervention. In this regard, Kim and Kim ( 2020 ) suggest focussing on optimising AI algorithms to boost index-tracking performance. Soleymani and Vasighi ( 2020 ) recognise the importance of clustering algorithms in portfolio management and propose a clustering approach powered by a membership function, also known as fuzzy clustering, to further improve the selection of less risky and most profitable assets. For this reason, analysis of asset volatility through deep learning should be embedded in portfolio selection models (Chen and Ge 2021 ).

AI and performance, risk, default valuation

Bankruptcy and performance prediction models rely on binary classifiers that only provide two outcomes, e.g. risky–not risky, default–not default, good–bad performance. These methods may be restrictive as sometimes there is not a clear distinction between the two categories (Jones et al. 2017 ). Therefore, prospective research might focus on multiple outcome domains and extend the research area to other contexts, such as bond default prediction, corporate mergers, reconstructions, takeovers, and credit rating changes (Jones et al. 2017 ). Corporate credit ratings and social media data should be included as independent predictors in credit risk forecasts to evaluate their impact on the accuracy of risk-predicting models (Uddin et al. 2020 ). Moreover, it is worth evaluating the benefits of a combined human–machine approach, where analysts contribute to variables’ selection alongside data mining techniques (Jones et al. 2017 ). Forthcoming studies should also address black box and over-fitting biases (Sariev and Germano 2020 ), as well as provide solutions for the manipulation and transformation of missing input data relevant to the model (Jones et al. 2017 ).

AI and cryptocurrencies

The use of AI in the cryptocurrency market is in its infancy, and so are the policies regulating it. As the digital currency industry has become increasingly important in the financial world, future research should study the impact of regulations and blockchain progress on the performance of AI techniques applied in this field (Petukhina et al., 2021 ). Cryptocurrencies, and especially Bitcoins, are extensively used in financial portfolios. Hence, new AI approaches should be developed in order to optimise cryptocurrency portfolios (Burggraf 2021 ).

AI and derivatives

This research stream examines derivative pricing models based on AI. A valuable research area that should be further explored concerns the incorporation of text-based input data, such as tweets, blogs, and comments, for option price prediction (Jang and Lee 2019 ). Since derivative pricing is an utterly complicated task, Chen and Wan ( 2021 ) suggest studying advanced AI designs that minimise computational costs. Funahashi ( 2020 ) recognises a typical human learning process (i.e. recognition by differences) and applies it to the model, significantly simplifying the pricing problem. In the light of these considerations, prospective research may also investigate other human learning and reasoning paths that can improve AI reasoning skills.

AI and credit risk in banks

Bank default prediction models often rely solely on accounting information from banks’ financial statements. To enhance default forecast, future work should consider market data as well (Le and Viviani 2018 ). Credit risk includes bank account fraud and financial systemic risk. Fraud detection based on AI needs further experiments in terms of training speed and classification accuracy (Kumar et al. 2019 ). Early warning models, on the other hand, should be more sensitive to systemic risk. For this reason, subsequent studies ought to provide a common platform for modelling systemic risk and visualisation techniques enabling interaction with both model parameters and visual interfaces (Holopainen and Sarlin 2017 ).

AI and investor sentiment analysis

Sentiment analysis builds on text-based data from social networks and news to identify investor sentiment and use it as a predictor of asset prices. Forthcoming research may analyse the effect of investor sentiment on specific sectors (Houlihan and Creamer 2021 ), as well as the impact of diverse types of news on financial markets (Heston and Sinha 2017 ). This is important for understanding how markets process information. In this respect, Xu and Zhao ( 2022 ) propose a deeper analysis of how social networks’ sentiment affects individual stock returns. They also believe that the activity of financial influencers, such as financial analysts or investment advisors, potentially affects market returns and needs to be considered in financial forecasts or portfolio management.

AI and foreign exchange management

This research stream investigates the application of AI models to the Forex market. Deep networks, in particular, efficiently predict the direction of change in forex rates thanks to their ability to “learn” abstract features (i.e. moving averages) through hidden layers. Future work should study whether these abstract features can be inferred from the model and used as valid input data to simplify the deep network structure (Galeshchuk and Mukherjee 2017 ). Moreover, the performance of foreign exchange trading models should be assessed in financial distressed times. Further research may also compare the predictive performance of advanced times series models, such as genetic algorithms and hybrid NNs, for forex trading purposes (Amelot et al. 2021 ).

Conclusions

Despite its recent advent, Artificial Intelligence has revolutionised the entire financial system, thanks to advanced computer science and Big Data Analytics and the increasing outflow of data generated by consumers, investors, business, and governments’ activities. Therefore, it is not surprising that a growing strand of literature has examined the uses, benefits and potential of AI applications in Finance. This paper aims to provide an accurate account of the state of the art, and, in doing so, it would represent a useful guide for readers interested in this topic and, above all, the starting point for future research. To this purpose, we collected a large number of articles published in journals indexed in Web of Science (WoS), and then resorted to both bibliometric analysis and content analysis. In particular, we inspected several features of the papers under study, identified the main AI applications in Finance and highlighted ten major research streams. From this extensive review, it emerges that AI can be regarded as an excellent market predictor and contributes to market stability by minimising information asymmetry and volatility; this results in profitable investing systems and accurate performance evaluations. Additionally, in the risk management area, AI aids with bankruptcy and credit risk prediction in both corporate and financial institutions; fraud detection and early warning models monitor the whole financial system and raise expectations for future artificial market surveillance. This suggests that global financial crises or unexpected financial turmoil will be likely to be anticipated and prevented.

All in all, judging from the rapid widespread of AI applications in the financial sphere and across a large variety of countries, and, more in general, based on the growth rate exhibited by technological progress over time, we expect that the use of AI tools will further expand, both geographically, across sectors and across financial areas. Hence, firms that still struggle with coping with the latest wave of technological change should be aware of that, and try to overcome this burden in order to reap the potential benefits associated with the adoption of AI and remain competitive. In the light of these considerations, policymakers should motivate companies, especially those that have not adopted yet, or have just begun to introduce AI applications, to catch up, for instance by providing funding or training courses aimed to strengthen the complex skills required by employees dealing with these sophisticated systems and languages.

This study presents some limitations. For instance, it tackles a significant range of interrelated topics (in particular, the main financial areas affected by AI which have been the main object of past research), and then presents a concise description for each of them; other studies may decide to focus on only one or a couple of subjects and provide a more in-depth account of the chosen one(s). Also, we are aware that technological change has been progressing at an unprecedented fast and growing pace; even though we considered a significantly long time-frame and a relevant amount of studies have been released in the first two decades of the XXI century, we are aware that further advancements have been made from 2021 (the last year included in the time frame used to the select our sample); for instance, in the last few years, AI experts, policymakers, and also a growing number of scholars have been debating the potential and risks of AI-related devices, such as chatGBT and the broader and more elusive “metaverse” (see for instance Mondal et al. 2023 and Calzada 2023 , for an overview). Hence, future contributions may advance our understanding of the implications of these latest developments for finance and other important fields, such as education and health.

Data availability

Full data are available from authors upon request.

The term AI winter first appeared in 1984 as the topic of a public debate at the annual meeting of the American Association of Artificial Intelligence (AAAI). It referred to hype generated by over promises from developers, unrealistically high expectations from end users, and extensive media promotion.

Since credit risk in the banking industry remarkably differs from credit risk in firms, the two of them are treated separately.

As this issue has not been addressed in the latest papers, we include these two papers although their year of publication lies outside the established range period.

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Bahoo, S., Cucculelli, M., Goga, X. et al. Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis. SN Bus Econ 4 , 23 (2024). https://doi.org/10.1007/s43546-023-00618-x

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Top 100 Finance Dissertation Topics Trending in the Year 2021

• July 28, 2021 July 28, 2021

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• Dissertation Topics on Covid-19 and its Impact on Financial Services
• What is the impact of Covid-19 on financial industry?
• Covid-19 and the rise of NPAs in Banks
• Are the companies becoming conservative regarding forecasts post Covid-19?
• Modified audit options in the financial world post Covid-19
• Is there a need for better insurance policy for the financial companies post Covid-19?
• Dissertation Topics on Cryptocurrency
• Can the market of crypto be followed with a technical analysis?
• How to measure the risks and returns in a cryptofinance market?
• Under the current supremacy regimen of Bitcoin and Ethereum, is there investment potential in alt coins?
• Are cryptocurrencies deemed as a reliable asset for swing trading?
• Why do banks oppose the advent of digital currency so fiercely?
• Dissertation Topics on Audit in Financial Services
• Can the BASEL rules be truly termed as successful to improve the liquidity and controls of the financial institutions?
• Function of automation and RPA in audit sector
• The role of auditors in the field of financial reporting: An Enron and WorldCom case study
• Auditors are the risk managers or cost centers for the bank: Discuss
• Sarbanes-Oxley Act and its impact on Auditor-client association in the UK
• Dissertation Topics on Risk Management: Capital, Liquidity, ALM, Models
• Financial crisis in the UK banking industry and liquidity risk management
• Risk management in the international banking functions in Europe: Its nature, scope and assessment
• Foreign exchange risk management within the financial institutions
• An evaluation of the credit risk management of the financial institutions
• How the provisions related to IFRS9 offer cushions to the financial service industry?
• Dissertation Topics on Internet Banking and Digital Journey for Banks
• The security challenges related to electronic banking transactions
• Can digitization be considered as a disruptive step towards banking sector?
• Will there be sustainability to Fintechs in the long run?
• What are the best security practices for the online transactions?
• Utility of Internet banking: Youth vs Elderly population
• Dissertation Topics on Ethics in Accounting
• Dilemma for the accountants: Ethics or profit?
• Rhetoric of rationality and the audit risk
• Does the accounting for a brand really matter?
• The independence of an auditor: Perception or reality?
• Transfer pricing and tax evasion: Teaching ethics to the accounting students of university
• Dissertation Topics on Microfinance
• The impact of microfinance on the SME sector
• The growth of microfinance in South Asia over a period of time
• The impact of microfinance in alleviating poverty and boosting economic growth in Bangladesh: A case study
• What are the determinant factors leading to the success of the institutions of microfinance?
• How the concept of microfinance is empowering women across the world?
• Dissertation Topics on Retail and Commercial Banking
• How the local commercial banks in Europe changed their practices over a period of time?
• The evolution of retail banking in India
• How the consumer demand is changing the preferences and strategies of SMEs in the US?
• Inventory management for mobile banking: A case study of the developing nations
• What is the relationship between price, equity, and performance in the banking industry?
• Dissertation Topics on Financing in Emerging Market
• FDI strategies in Asia and Europe: A comparative study
• What is the link between economic growth and population: A case study of China
• The evolution and the current state of investment banking in the emerging markets
• What are the challenges faced by the financial institutions in an emerging economy?
• The perception and attitude of the investors from a developed country towards the investment opportunities in the emerging markets
• Dissertation Topics on Alternative Investment
• Hedge funds: An alternative investment option for the institutional investors
• Private equity market and its different types of investors
• Investment diversification through alternative investment vehicles
• The timeline of mutual funds and their growing popularity
• Hedge funds: Risk diversifiers, return enhancers or both?
• Miscellaneous Finance Dissertation Topics on International Affairs
• Investing in the energy sector of Russia: The bulwarks and opportunities
• Foreign investment and its impact on the economic growth of Zambia
• The theory of cognitive moral development and the moral maturity of finance professionals
• Do microfinance companies really come to the aid of the poorest of the poor?
• The economic reforms in Saudi Arabia and its direct influence on the foreign direct investment
• Financial advancements in Europe and Asia: A comparative study
• What are the negative influences of microfinance in the developing economies?
• The merits and demerits of aggressive economic policies of China
• The restructuring of banking sector in Brazil: A case study
• The future of mutual funds in emerging economies
• An assessment of the financial crisis 2007-08: Could it have been eschewed?
• The merits and demerits of private equity investment in European Union post Brexit
• Can cryptocurrency actually have the capacity to demonetize the world?
• What is the future of banking system innovation in the United States?
• Digital banking issues faced by the elderly and the optimal measures to resolve them
• Do financial institutions somehow finance terrorism unknowingly?
• Forecasting the next catastrophe of financial crisis: Could 2021 turn out to be the year?
• Are the profits made by financial institutions unethical? If yes, why?
• How Brexit impacted the ability of the banks in the UK to hire talented specialists?
• What do you mean by the balanced scorecard? Why is it so significant to the legal firms?
• What are the modern accounting standards? Do they actually live up to the expectations of the regulator?
• The accounting education programs in the US and the UK: A comparative study
• An assessment of the top 3 CSR issues in the modern banking systems
• Here is how the Internet banking would look like in the next 10 years
• An assessment of the trade blocks (BRIC) and their influence on the banking sector
• PATRIOT Act of the United States and its impact on the international banking
• How significant is the flow of credit to an agronomic industry?
• The 5 ways through which the IT has transformed the financial sector
• An essay on the 5 most significant traits of a highly successful microfinance institution
• IFRS and its implementation in the UK: The concerns and the reactions
• Is there a connection between corporate strategy and capital structure?
• Debt trap policy of China: Is it a conventionally practiced policy of Eco-terrorism or a new one?
• Discuss the real estate industry of China from the viewpoint of a banker
• Which banks are conventionally more profitable, the domestic or the foreign ones?
• The effective management of cash flow at Groupe Societe Generale
• An assessment of the financial statements of MasterCard and VISA
• The culture of cooperation and opportunism in the venture capital financed companies
• The gender and the development happening through western eyes: An evaluation of microfinance as the solution of the west to poverty, third world women and neoliberalism
• A multi-factor form quadratic stochastic volatility model and its implementation in insurance and finance
• A quantitative study of innovation-driven investment and its performance management on corporate fiscal returns
• American venture capitalism in the Post-American world: A case study
• Exploring the opportunities other than oil in Saudi Arabia and the UAE for foreign direct investment (FDI)
• Asset levels of the service based decision support systems for infrastructure investment in Singapore
• Bulwarks to investment and credit to the minority business entrepreneurs: An investigation of the Rockford, IL, metropolitan statistical area
• Economic growth through foreign direct investment: The case study of Nigeria
• Increasing the values of a firm through the derivatives for hedging
• Why the real estate sector in Dubai undergoing financial crisis?
• The connection between the sentiment of investor and stock volatility: A case study of the USA and China
• How the awareness and influence of financial services to the low income group endorsed the economy of Malaysia?

    100. The impact of firm characteristics on the capital structures of SMEs: A Hong Kong case study

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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Top 140 Finance Research Paper Topics

Why finance topics? The search for interesting finance topics is a constant one. Of course, this is understandable because knowledge of hot topics in finance puts you ahead of the game. Students or researchers who major in business will, at one point or the other in their careers, make presentations, and submit research papers, essays,or help with dissertation or projects. With the headache of writing these papers aside, the challenge of picking finance topics always looms around. We have, therefore, carried out extensive research to present you with these 30 finance topics that will make your paper worth reading! When in doubt, this list of finance topics will surely come in handy to shed some light on that darkness!

Finding Excellent Topics in Finance

We offer you 30 researchable topics in finance. But why should we only catch fish for you if we can teach you how to fish too? The need to find unique topics in finance is on the increase. Here are some excellent tips that will help you choose appropriate finance topics:

• Find out unanswered questions from previous research works or develop on areas that require additional study.
• Read several theses to form ideas.
• Check economics topics . They may be more general but you can narrow down some of them.
• Search online for related topics that are unique, or make them unique to suit your purpose.
• Discuss your chosen topic with other students or people who have experience writing dissertations asking for their input.

Research Topics In Finance

In financial research, unique topics are pivotal to the overall success of the study. The reason for this is simple. Now put yourself in the shoes of professors who have read hundreds of theses and essays. They already know common topics that students like to write or work on. A hot research topic in finance will surely catch the attention of your professor and will likely earn you better grades. Writing finance research papers becomes much easier when you have superb finance research topics.

Here is a finance research topics list that will spark people’s interest in your research work and make your finance research paper worth reading! Ready for these research topics in finance? Read on!

• Merger and Acquisition: An Analytical Study of the Benefits and Set-backs.
• Capital Asset Pricing Model: Possible Solutions to its Inadequacies.
• Global Financial Crisis: A Critical Study of the Role of Auditors and Stakeholders.
• The Impact of Manipulating the Commodity Market on Future Commerce.
• Continuous-time Models: An exhaustive Comparative Analysis of its Application in Divers financial Environments.
• How Speculations Undermine the Stability of Banking in Asian Markets.
• Branding: Its Effect on Consumer Behavior.
• An effective strategy for managing inventory and controlling your budget.
• An analytical report on the various investments in tax-saving products.
• Using a systematic investment strategy to build stability for retail investments.
• How income tax is planned and implemented in India’s economy.
• A detailed analysis of how the Indian banking system operates.
• How does multi-level marketing work in different economies around the world?
• A detailed report on electronic payment and how it can be improved.
• A case study regarding senior citizen investment portfolios.
• Are there potential risks and rewards when comparing savings to investments?
• Is ratio analysis an effective component of financial statement analysis?
• How the Indian economy functions with its current banking operations.

Finance Research Topics For MBA

Here are some great finance research topics you can use toward your MBA. It’s sure to intrigue your professor and get you to look at finance from a different perspective.

• Investment analysis of a company of your choice.
• A detailed report on working capital management.
• Financial plans and considerations for saving taxes and salaried employees.
• A detailed analysis of the cost and costing models of the company of your choice.
• The awareness of investments in financial assets and equity trading preference with financial intermediaries.
• The perspective of investors and their involvement with life insurance investments.
• A detailed analysis of the perception of mutual fund investors.
• The comparative study between UIL and the traditional products.
• A detailed report on how the ABC company manages cash.

Corporate Risk Management Topics

These are some key topics you can use relating to corporate risk management.

• A detailed report on the fundamentals of corporate risk management.
• The analytical concepts relating to effective corporate and financial management within a company.
• How does corporate risk management affect the financial market and its products?
• What are risk models and how are they evaluated?
• How is market risk effectively measured and managed in today’s economy?
• How can a company be vigilant of potential credit risks they can face?
• What are the differences between operational and integrated risks in the corporate world?
• Is liquidity an effective strategy to lower financial risk to a company?
• How risk management can connect with and benefit investment management.
• The current issues that are affecting the modern marketplace and the financial risks they bring.

Healthcare Finance Research Topics

These are some key topics you can use relating to healthcare finance research.

• Is it better for the government to pay for an individual’s healthcare?
• The origins of healthcare finance.
• An analysis of Canada and their healthcare finance system.
• Is healthcare financing a right or a privilege?
• The changing policies of healthcare in the U.S.
• Can healthcare be improved in first-world countries?
• Can the healthcare system be improved or remade?
• How much influence does the government have on healthcare in a country?
• The impact of growing global health spending.
• Is free healthcare achievable worldwide?

Corporate Finance Topics

Corporate finance deals with processes such as financing, structuring of capital, and making investment decisions. It seeks to maximize shareholder value by implementing diverse strategies in long and short-term financial planning.

Corporate finance research topics broadly cover areas like tools for risk management, trend research in advanced finance, physical and electronic techniques in securities markets, research trends in advance finance, investment analysis, and management of government debt. The following corporate finance topics will surely minimize any risk of mistakes!

• Using the Bootstrapped Interest Rates to Price Corporate Debt Capital Market Instruments.
• Corporate Organizations: The Impact of Audit Independence on Accountability and Transparency.
• Buybacks: A Critical Analysis of how Firms can Buy Back at Optimal Prices.
• Merge and Acquisitions: Reasons why Firms still Overpay for bad Acquisitions.
• Corporate Finance: Ethical Concerns and Possible Solutions.
• Understanding the investment patterns relative to smaller and medium-capitalization businesses.
• A detailed analysis of the different streams of investment relating to mutual funds.
• Equity investors and how they manage their portfolios and perception of potential risks.
• How does investor preference operate in the commodity market in Karvy Stock Broking Limited?
• An analysis of the performance of mutual funds in the public and private sectors.
• Understanding how Videcon manages its working capital.
• The Visa Port trust and how it conducts ratio analysis.
• How the gold monetization scheme has affected the Indian economy and banking operations.
• How does SWIFT work and what are the potential risks and rewards?
• A detailed analysis of the FMC and SEBI merger.

Business Finance Topics

Every decision made in a business has financial implications. It is, therefore, essential that business people have a fundamental understanding of finance. To show your knowledge, you must be able to write articles on finance topics in areas such as financial analysis, valuation, management, etc. Here are some juicy business finance topics!

• Application of Business Finance: Its importance to the Business Sector.
• The Importance of Business Finance in the Establishment of Business Enterprises.
• Modernization of Business: Roles of Business Finance in Business Modernization.
• A detailed study on providing financial aid to self-help groups and projects.
• Is tax an effective incentive for selling life insurance to the public?
• Understanding how the performance of mutual funds can change within the private and public sectors.
• Is there a preference for different investment options from financial classes?
• A detailed analysis of retail investors and their preferences and choices.
• A study on investors and their perspective on investing in private insurance companies.
• How analyzing financial statements can assess a business’s performance.
• Increasing the accountability of corporate entities.
• Ethical concerns connected to business finance and how they can be managed.
• The level of tax paid by small to medium businesses.

International Finance Topics

As the world is now a global village, business transactions occur all around the world. No more are we limited to local trade, and this is why the study of international is essential and relevant. Here are some international finance topics that will suit your research purpose!

• Stock Exchange: How Important are the Functions of a Bank Office?
• Global Economic Crises: Possible Precautions to prevent Global Financial crisis.
• Bond Rating: the Effect of Changes on the Price of Stocks.
• How the Banking Industry can Decrease the Impact of Financial Crisis.
• Is it possible for a country to budget funds for healthcare for the homeless?
• The negative impact of private healthcare payments on impoverished communities.
• What sectors in healthcare require more funding at the moment?
• The dilemma of unequal access to adequate healthcare in third world countries.
• Can cancer treatment be more inexpensive to the public?
• The problem with the high pricing of medication in the U.S.
• Is there a better way to establish healthcare financing in the U.S?
• What are the benefits of healthcare finance systems in Canada and the UK?
• How can third-world countries improve their healthcare systems without hurting their economy?
• Is financing research a priority in healthcare and medicine?
• Does free healthcare hurt the tax system of a country?
• Why is free and privatized healthcare present in different economies?
• How does government funding affect healthcare finance systems?
• How do patient management systems work?
• Where does affordable healthcare financing fit in growing economies?
• The economic impact of COVID-19 in various countries.
• The healthcare policies of the Serbian government.

Finance Research Paper Ideas

Writing a research paper requires an independent investigation of a chosen subject and the analysis of the remarkable outcomes of that research. A finance researcher will, therefore, need to have enough finance research paper topics from which to choose at his fingertip. Carefully selecting a finance thesis topic out of the many finance research papers topics will require some skill. Here are some exciting finance paper topics!

• Behavioral Finance versus Traditional Finance: Differences and Similarities.
• Budgetary Controls: The Impact of this Control on Organizational performance.
• Electronic Banking: The Effect of e-Banking on Consumer Satisfaction.
• Credit and Bad Debts: Novel Techniques of management in commercial Banks.
• Loan Default: A Critical Assessment of the Impact of Loan Defaults on the Profitability of Banks.
• A detailed analysis of the best risk management methods used in the manufacturing industry.
• Identifying and measuring financial risks in a derivative marketplace.
• Exploring the potential risks that can occur in the banking sector and how they can be avoided.
• The risks that online transactions bring.
• What are the methods used to ensure quantitive risk management is achieved?
• A better understanding of policy evaluation and asset management.
• What makes traditional finance so different from behavioral?
• The significance of budgetary control in a corporate organization.
• How do loans benefit the profitability of banks?
• How do commercial banks assist their clients that are in bad debt?
• The various considerations we need to be aware of before making investment decisions.

Personal Finance Topics

Personal finance covers the aspects of managing your money, including saving and investing. It comprises aspects such as investments, retirement planning, budgeting, estate planning, mortgages, banking, tax, and insurance. Researching in this area will surely be of direct impact on the quality of living. Here are some great personal finance topics that are eager to have you work on them!

• Evaluation of Possible Methods of Saving while on a Budget.
• The Effect of Increase in Interest Rate and Inflation on Personal Finance.
• Benefits of Working from Home to both Employers and Employees.
• Will dental services be considered an essential medical service soon?
• Is affordable or free healthcare a right that everyone should be entitled to?
• The best ways to save money while on a tight budget.
• What happens to personal finance when inflation and interest rates rise?
• The financial benefits of working from home.
• Does innovations in personal finance act as an incentive for households to take risks?
• A detailed analysis of credit scores.
• The importance of credit and vehicle loans.
• A detailed analysis of employee benefits and what should be considered.
• The effect of tax on making certain financial decisions.
• The best ways to manage your credit.
• The difficulties that come with mobile banking.

Finance Topics For Presentation

Sometimes, you may need to present a topic in a seminar. The idea is that you can whet the appetite of your audience with the highlights of your subject matter. Choosing these finance seminar topics requires a slightly different approach in that you must be thoroughly familiar with that topic before giving the presentation. Interesting and easy-to-grasp finance topics are, therefore, necessary for presentations. Here are some topic examples that fit perfectly into this category.

• Analysis of the Year-over-Year Trend.
• Maximizing Pension Using Life Insurance.
• The Architecture of the Global Financial System.
• Non-communicable diseases and the burden they have on economies.
• Is there a connection between a country’s population and its healthcare budget?
• The spending capability of medical innovations in a third-world economy.
• The long-term effects of healthcare finance systems in the U.S.
• A detailed analysis of pharmaceutical marketing in eastern Europe.
• Understanding the reduction in medical expenses in Greece.
• Private payment for healthcare in Bulgaria.
• A complete change in healthcare policy worldwide. Is it necessary?
• The significance of electronic banking on the public.
• The evolution of banking and its operations.

So here we are! Surely, with this essay on finance topics that you have read, you’ll need only a few minutes to decide your topic and plunge into proper research! If you need professional help, don’t hesitate to contact our economics thesis writers .

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Research Topics

Esg investments.

ESG is an acronym that combines E nvironmental, S ocial, and corporate G overnance aspects of businesses capturing the non-financial characteristics of firms that may well have material impact on investment returns. ESG metrics of companies are increasingly critical for investment decision-makers.

The Global Sustainable Investment Alliance (GSIA) reported its latest statistics on the global dominance of sustainable investments: by 2020, every third dollar was invested in the financial markets in a sustainable form, in line with the ESG approach. While only 21 per cent of assets under management were ESG-compliant in 2012, the recent surge in ESG investments meant growth exceeded 50 per cent over the six years. The popularity of ESG investing has since grown significantly and is becoming mainstream.

Based on the GSIA  classification, negative screening remains the most popular ESG-based investment strategy. At the same time, thematic investments benefitted from the largest growth of fund inflows in recent years: from 2016 to 2018, thematic funds quadrupled their holdings, now exceeding one trillion USD in assets.

Climate Finance

The discipline of Climate Finance relates to financing of project that help climate change adoption and mitigation efforts.

Green Investments

Investment instruments that specifically have environmental impact targets are labelled as green. Mostly common instruments are Green Bonds, these investments' payout is often contingent upon certain compliance with green goals. By 2020, more than USD 10bn was invested in 172 green bond in 20 different currencies. Their popularity is ever growing among investors.

Biodiversity Finance

Biodiversity finance is a new discipline within nature finance that concerns the funding, and financing the conservation of nature, with particular focus on stopping biodiversity loss in highly-sensitive ecological areas. Unlike climate measures, biodiveristy loss is geography focused, and is yet to develop measurements. Although the existing data vendors rely on the measure of mean species abudance over a space, there is a need for more nuanced methodology to capture nature loss. A starting point for business activities is to conduct a materiality assessment to evaluate the impact of a company's activity on biodiversity, and ecosystems, and the dependenies it has on nature.

Sustainability Risk

Sustainability risk is known as the risk event stemming from environmental or social factors that have a material negative impact on the value of an investment. The EU directive requiring companies to disclose sustainability related risk was enacted in 2021. The topic's importance derives from the shared responsibility of all market participants to tackle sustainability issues. The methodology for measuring sustainability-related risk and including it in finance decisions is an evolving discipline.

Greenwashing or Impact Washing

The act of claiming green efforts or targets, but in fact are misleading, either in an intentional manner or casued unintentionally. Impact washing is similar to greenwashing but is specific to investments. It is aimed at appealing to the ESG conscious investors, but the investment fails to demonstrate achieving of claimed sustainability targets. Recently, greenwashing scandals have surfaced in the investment industry as regulators become more stringent in their scrutiny.

Nature-based Solutions (NbS)

Resolution 069 of IUCN defines NbS as actions to protect, manage and restore natural or modified ecosystems, which address societal challenges, effectively and adaptively, providing human well-being and biodiversity benefits. Societal challenges being climate change, natural disasters, social and economic development, human health, food security, water security, ecosystem degradation and biodiversity loss.

Nature-positive economy

Defined by CISL as an economy in which public and private sector actors, through choice and incentive, take action at scale to reduce and remove the drivers and pressures fuelling the degradation of nature, actively improving the state of nature (natural capital) and the ecosystem services it provides. 

• Open access
• Published: 20 December 2023

Emerging new themes in green finance: a systematic literature review

• H. M. N. K. Mudalige   ORCID: orcid.org/0000-0002-4497-4750 1  

Future Business Journal volume  9 , Article number:  108 ( 2023 ) Cite this article

3694 Accesses

Metrics details

There is a need for an extensive understanding of the emerging themes and trends within the domain of green finance, which is still evolving. By conducting a systematic literature review on green finance, the purpose of this study is to identify the emerging themes that have garnered significant attention over the past 12 years. In order to identify the emerging themes in green finance, bibliometric analysis was performed on 978 publications that were published between 2011 and 2023 and were taken from the databases of Scopus and Web of Science. The author examined annual scientific production, journal distribution, countries scientific production, most relevant authors, most frequent words, areas where empirical research is lacking, words' frequency over time, trend topics, and themes of green finance. The outcome of the review identified the following seven themes: (i) green finance and environmental sustainability; (ii) green finance and investments; (iii) green finance and innovation; (iv) green finance policy/green credit guidelines; (v) green finance and economy; (vi) green finance and corporate social responsibility; (vii)trends/challenges/barriers/awareness of green finance. The analysis of these emerging themes will contribute to the existing corpus of knowledge and provide valuable insights into the landscape of green finance as it evolves.

Introduction

Cities will face their greatest challenges ever during the next 30 years, and three-quarters of the world's population will reside in urban areas by 2050 due to the unparalleled rate of urbanization as a result of population growth, resource scarcity, such as peak oil, water shortages, and food security [ 100 ].

One of the main challenges in building and maintaining sustainable cities is discovering the sources required to fund vital infrastructure, development, and maintenance activities that have a sustainable future. To achieve the creation of sustainable cities, there is a need for green projects via green financial bonds, green banks, carbon market tools, other new financial instruments, new policies, fiscal policy, a green central bank, fintech, community-based green funds, and expanding the financing of investments that provide environmental benefits [ 26 , 78 ].

It is evident that green financing plays a crucial role in promoting sustainable initiatives. Thus, a transition from a rising economy to a green economy necessitates that a country's leadership offers green financing [ 112 ]. To assure green economic growth, nations around the world have invested in green projects to promote, invent, and employ environmentally friendly technologies to safeguard the environment and maximize environmental performance [ 55 ]. Because of new stakeholders' and institutions' understanding of environmental issues, regulatory authorities are likely to seek out extra ecologically acceptable financial resources. In an effort to establish environmental legitimacy, this type of environmental proactivity will be required when new methods of providing financial resources and green financing arise.

In numerous ways, the impact of adopting green financing is proven. First, green finance provides financial support for firms engaged in green innovation, including the purchase of green equipment, the introduction of new environmentally efficient technologies, and the training of their personnel. Second, green funding from various projects can assist stakeholders (organizations, governments, and regulators) in spending R&D funds on environmental challenges and minimize the associated risk with green legislation. Lastly, green policies have higher costs than conventional practices, and green finance can assist an organization in covering these expenses without encountering significant financial obstacles. As a result, green finance-driven economic growth can significantly support green policies, lessen environmental pollution, and build sustainable cities [ 128 ].

There have previously been systematic literature reviews conducted in the green finance area. However, a study's reliance on one database can exclude some recent developments in green finance from its analysis [ 93 ]. Findings from several databases could be compared and contrasted to create a more all-encompassing view of the area. Therefore, this study focuses on using Scopus and WoS databases.

Though additional methods, such as systematic literature reviews (SLR) and more complex network analyses such as co-occurrence of index terms, citations, co-citations, and bibliometric coupling, are available, previously conducted studies used a fundamental bibliometric technique [ 23 ]. A more detailed picture of the green finance study setting may emerge from an examination of the identification of various themes.

As part of a systematic review of the literature concerning emerging trends in green finance, it is critical to ascertain the dominant themes that are present in the field. By adopting this methodology, an intentional emphasis is placed on maintaining the review's relevance and excluding any studies that are obsolete. In addition, by identifying and classifying these themes, one can gain significant knowledge regarding the ever-changing characteristics of green finance, thereby illuminating the latest advancements and patterns. A study conducted by Pasupuleti and Ayyagari [ 99 ] identified different themes in green finance, but the researchers were only focused on polluting companies. By amalgamating insights from the literature review, one can attain a holistic comprehension of the current state of research in the field of green finance. Additionally, this process identifies areas where additional inquiry is necessary. Engaging in such an undertaking provides advantages not only to the scholarly community but also carries practical implications for policymakers, practitioners, and investors, assisting them in formulating effective policies and investment strategies and making well-informed decisions.

Green finance research is growing rapidly. However, the rising themes and trends in green finance literature must be comprehended. A comprehensive literature review can summarize current knowledge, identify research gaps, and identify the field's most relevant topics. This study seeks to uncover green finance's emerging themes through a rigorous literature review. This research aims to advance green finance knowledge by synthesizing and analyzing a wide range of scholarly articles.

Methods and methodology

Study selection process and methods.

In this study, a systematic literature review (SLR) was applied. It used inclusion criteria, analysis techniques, and a more objective method of article selection. As recommended for SLRs [ 65 ] with regard to the article selection process, the PRISMA article selection steps were adhered to. The steps are "identification," "screening," and "included". The steps that were taken in this study are shown in Fig.  1 .

PRISMA article selection flow diagram. Note : Search algorithm; “green finance” . Sources (s) Authors Construct, 2023

In the identification phase, the search terms, search criteria, databases, and data extraction technique are chosen. The keyword to use in the search was "green finance" as the study is aimed at identifying emerging themes in green finance.

The identified articles need to be screened in accordance with the PRISMA guidelines. The tasks carried out at the screening were the screening, retrieval, and evaluation of each article's eligibility. According to Priyashantha et al. in [ 103 ], articles in each task that did not meet the inclusion criteria were removed. The "empirical studies" published in "Journals" from "2011–2023" in "English" were the inclusion criteria for screening the articles. In 2023, up to May, the journal articles were chosen.

This screening was carried out both manually and automatically. Utilizing Scopus' and Web of Science's (WoS) automatic article screening features by study type, language, report type, and publication date, articles achieving the inclusion criteria "empirical studies" published in "English" "journals" from "2011–2023″ were included. The other publication types such as conference papers, book chapters, reviews, research notes, editor's comments, short surveys, and unpublished data, as well as non-English articles and articles published within the considered year range, were excluded. The full versions of the screened articles were then retrieved for the eligibility assessment, the next stage of screening. The author manually evaluated each article's eligibility.

Study risk of bias assessment

Researcher bias in article selection and analysis lowers the quality of reviews [ 8 , 102 ]. Avoiding bias in article selection and analysis requires using a review protocol, adhering to a systematic, objective article selection procedure, using objective analysis methods [ 8 , 102 ], and performing a parallel independent quality assessment of articles by two or more researchers [ 8 ]. By adhering to all of these requirements, the risk of bias in the articles was removed.

Methods of analysis

Biblioshiny and VOSviewer were used for bibliometric analysis. Green finance literature was captured by Scopus and WoS. These databases were used exclusively to get a representative sample of journal articles to study green finance articles. The data were collected and analyzed using Biblioshiny. Select databases can be systematically extracted and analyzed with the software. It collects year-by-year article distribution, journal distribution, country-specific scientific production, most relevant authors, most frequent words, word frequency over time, trend topics, density visualization, etc.

Trends and patterns were found by analyzing green finance paper distribution by year. This analysis shows green finance research's growth. By analyzing article distribution by year, we may also establish green financing and rising theme trends. To identify green finance research publications, article distribution was studied. Academic journal distribution can indicate green finance's prominence in various academic journals. Analyzing scientific production by region reveals regional green finance research tendencies. Scientific production across nations identifies knowledge-producing regions.

Analyzing influential green finance authors helps identify their contributions. This strategy acknowledges influential scholars. The research's most frequently used words reveal the fundamental questions and ideas of environmentally responsible economics. This analysis reveals the discipline's primary topics and studies. By counting words, it may focus on green finance's most important and widely used components. Word frequency can show how green finance's focus has shifted. By tracking word usage, it can identify trending topics. This analysis reveals changing green finance research priorities. Biblioshiny explores green financial trends. This study reveals new topics, research gaps, and subject interests. The trend themes allow us to evaluate green finance studies.

Results and findings

Study selection.

The PRISMA flow diagram illustrates that during the identification step, 528 articles from the WoS database and 1183 articles from the Scopus database that include the term "green finance" were identified. There were 402 duplicates, which were removed. The overall number of articles remained at 1302 at that point. Further attempts were made to include papers on empirical investigations in the final versions that were published in English. 34 non-English articles were thus disregarded. In addition, 295 papers from conferences, book chapters, reviews, news articles, notes, letters, abstracts, and brief surveys were not included. Two articles were disqualified because they were published before 2011. The next step was to retrieve the remaining 978 articles and transfer their pertinent data to an MS Excel file, including the article's title, abstract, keywords, authors' names and affiliations, journal name, citation counts, and year of publication. After that, each article was examined by a third party to determine whether it met the requirements for its eligibility.

Study characteristics

Main information.

This study examined 978 studies by 1830 authors from 59 countries. They've been published in 281 publications. The average number of citations each article received was 12.37. There were a total of 2206 keywords and 44,712 references. This information is detailed in Table  1 .

Annual scientific production

The fluctuations in green financing for scientific production are depicted in Fig.  2 . In 2011, two articles were published that demonstrated interest in this research. No publications were released in 2012, indicating a paucity of research or interest. The trend persisted in 2013 with two articles. One publication appeared in 2014, indicating a halt in research. Since 2015, scientific output has gradually increased. In 2015, three articles contributed to the development of green finance research. Two articles survived in 2016. With eleven articles published in 2017, green finance has become a significant area of study. In 2018, 23 articles were published; in 2019, there will be 42. With 45 publications in 2020, green finance research remains robust. Green finance research increased to 132 publications in 2021. This significant increase in articles on the subject indicates a growing interest in the matter. The publication of 403 research articles in 2022 represents a notable increase. This increase reflects the expanding literature on green finance and its academic significance.

Year-wise research article distribution. Source (s): Author created, 2023

Journal distribution

Table 2 consists of a list of journals that were included in the sample and had more than six relevant papers published inside the journals. The majority of the journals that publish articles relating to green finance are, unsurprisingly, those that focus on environmental science, renewable energy, and sustainability. This is despite the fact that finance is considered an essential component of green financing. Not a single journal in the field of finance was able to attract more than 10 papers.

Based on the number of papers, Environmental Science and Pollution Research emerges as the top journal, demonstrating a strong focus on comprehending the intersection between environmental science, pollution, and financial aspects. The prevalence of journals focused on renewable energy and sustainability, each of which publishes 50 papers, demonstrates the growing interest in examining the financial aspects of sustainable development and renewable energy sources. The fact that Resources Policy was included in the list of 49 papers indicates that a significant emphasis was placed on understanding the financial implications of resource management and extraction.

Green finance is interdisciplinary in nature, exploring the connections between finance and various environmental issues, as evidenced by the existence of interdisciplinary journals like Frontiers in Environmental Science. The existence of journals like Finance Research Letters and Economic Research-Ekonomska Istrazivanja highlights the importance of economic and financial analysis in the context of green finance.

Countries scientific production

The analysis of region frequencies in the provided data in Fig.  3 reveals intriguing patterns and highlights the varying levels of research focus in various countries. The analysis is focused on the top ten countries for scientific production on green finance.

China is the part of the world most frequently mentioned, with a striking frequency of 993. This suggests a significant research interest in comprehending and analyzing diverse aspects of China's economy, policies, and development. Given China's status as the world's most populous nation and its growing global influence, it is unsurprising that researchers have devoted considerable effort to examining China's position in various fields, including finance, sustainability, and innovation.

Pakistan follows with a frequency of 79, indicating a notable but relatively lower research emphasis. Researchers may have investigated particular Pakistan-related topics, such as its economy, governance, or social issues. Pakistan may be of particular interest to a subset of researchers, or there may be a paucity of relevant literature in the analyzed dataset.

With a frequency of 60, the UK is the third-most-mentioned region. This demonstrates a sustained interest in researching various aspects of the UK, such as its economy, financial sector, and policies. It is possible that the historical significance of the UK, particularly in terms of finance and international relations, contributed to its prominence in literature.

Most relevant authors

The prominent and active contributors to the discipline are shown in Fig.  4 . Wang Y has significantly added to the body of literature. The top authors have a constant record of publishing, which shows a dedication to knowledge advancement and suggests a high level of expertise in their field of study.

In this section, the findings that conform to the aims of the research are reported. The conclusions were generated through the use of trend themes, keyword co-occurrence analysis, "most frequent words," and "word frequency over time." During the course of the investigation, both the "keyword co-occurrence; network visualization" and the "density visualization" methods were applied.

Most frequent words

The analysis of the most frequent words sheds light on the emerging themes in the field of green finance, as illustrated in Table  3 and Fig.  5 . A significant emphasis on China, which appears 253 times in the literature, is one of the important observations. This indicates that China's initiatives and role in the context of sustainable finance and green investment are gaining increasing recognition. China's approach to green finance and its potential implications for global sustainability initiatives are likely the primary focus of researchers and policymakers.

The term "finance" appears 122 times, emphasizing the importance of financial mechanisms and instruments to the advancement of green initiatives. This emphasizes the significance of financial institutions, policies, and frameworks that support environmental protection and sustainable development. The frequency of the term "investment" (103) emphasizes the significance of allocating financial resources to environmentally friendly businesses and initiatives.

The 105 occurrences of "sustainable development" indicate the close relationship between green finance and broader sustainability goals. This indicates that researchers and practitioners recognize the need to align financial decisions with environmental, social, and governance (ESG) factors in order to achieve long-term sustainable development objectives.

The terms "green economy" (75) and "environmental economics" (57) refer to the integration of environmental considerations into economic systems and decision-making procedures. This emphasizes the importance of transitioning to environmentally sustainable economic models and policies.

The frequency of terms such as "carbon," "carbon emissions," and "carbon dioxide" (55, 55, and 51 times, respectively) indicates a focus on mitigating greenhouse gas emissions and addressing climate change via financial mechanisms. This is consistent with the worldwide drive for decarbonization and the transition to low-carbon economies.

In addition, the terms "innovation" (71), "impact" (67), and "efficiency" (49) emphasize the significance of technological advancements, measurable outcomes, and resource optimization in green finance. These ideas illustrate the ongoing pursuit of innovative strategies and solutions to promote positive environmental impact while maximizing resource utilization.

The terms "sustainability" (44), "policy" (49), and "financial system" (41) highlight the need for policy frameworks and a robust financial system to facilitate the incorporation of sustainability considerations into mainstream finance. These themes emphasize the critical role that regulations, incentives, and institutional arrangements play in promoting green finance practices and nurturing a sustainable economy.

In addition, the terms "climate change" (50) and "alternative energy" (42) suggest an emphasis on addressing climate-related issues and investigating renewable and sustainable energy sources. This demonstrates an acknowledgment of the role of green finance in the transition to a low-carbon, resilient future.

The relationships between the keywords depicted as nodes are displayed in Fig.  6 's keyword co-occurrence network visualization. The link shows how each keyword relates to the others. In particular, the thickness of the line indicates how strong the relationship is. As a result, Fig.  8 illustrates how China and green finance are connected by a thicker line, showing that the majority of green finance research is carried out in China. Additionally, the connection between finance, sustainable development, and investments in green finance shows their connection to green finance. In Fig.  6 , the nodes are grouped into the red, green, and blue clusters. These clusters contain the keywords listed in Table  3 for each one. The various clusters in Fig.  6 demonstrate how different areas of research had distinct effects on green financing. When keywords are grouped together, it indicates that the topics they refer to are quite likely to be the same. As a result, the red, green, and blue clusters in Fig.  6 highlight common themes, while Table  4 provides explanations for the clusters.

The keyword co-occurrence network visualization

Areas where empirical research is lacking

Figure  7 displays the density visualization map that the VOSviewer generated. The VoSviewer manual states that a node with a red background denotes sufficient research for established knowledge and that it is evident that more study on green finance is still needed. On the other hand, keyword nodes with a green background show that there hasn't been much research on those particular keywords. Other than finance and China, the other keywords in the figure are therefore in the green background, which denotes insufficient research.

The keyword co-occurrence density visualization

Word’s frequency over time

The analysis of words' frequency over time in Fig.  8 reveals a number of significant trends. Beginning in 2018, the frequency of the term "China" increases considerably, with a significant rise in 2022 and a peak of 253 occurrences in 2023. This indicates a growing emphasis on China's role in green finance and its expanding prominence in the academic literature.

The persistent occurrence of the term "finance" over the years indicates the sustained significance of financial mechanisms and instruments in the context of green finance research. Its increasing frequency over time demonstrates the continued emphasis placed on financial aspects of the field.

The consistent growth of the term "sustainable development" from 2016 to 2019 indicates a growing recognition of the connection between green finance and broader sustainability objectives. However, after 2019, its occurrence remains comparatively stable, indicating that sustainable development has become a well-established and consistent theme in the literature.

Similarly, the term "investment" has maintained a consistent presence throughout the years, indicating a continued emphasis on allocating financial resources to green and sustainable initiatives. Its frequency fluctuates but remains relatively high throughout the period under consideration.

The frequency of the term "economic development” increased gradually until 2021, after which it remained relatively stable. This indicates that researchers have acknowledged the need to incorporate economic development and sustainable practices, resulting in a continued emphasis on this topic.

Similar to the term "investments," it has maintained a consistent presence throughout the years. This demonstrates a persistent desire to investigate investment opportunities and strategies within the context of green finance.

The frequency of the term "green economy” increased until 2020, after which it stabilized. This demonstrates an ongoing commitment to transitioning to a greener and more sustainable economy.

The terms "innovation" and "impact" have exhibited a general upward trend over the years. This suggests that innovative approaches to measuring the impact of green finance initiatives and projects are gaining importance.

The term "green finance" has been used significantly more frequently, particularly after 2021. This demonstrates the increasing interest and focus on the specific discipline of green finance, reflecting its emergence as a distinct research area within the context of sustainable finance as a whole.

Trend topics

Insights into novel areas and their developments over time can be gained from an analysis of trend themes using author keywords in the bibliometric data, as shown in Fig.  9 .

Trend Topics

There are nine times where the "Paris Agreement" is mentioned as a subject. It was consistently present from 2019 to 2022, demonstrating a strong interest in comprehending the ramifications and execution of this global climate agreement. The Paris Agreement's effects on environmental regulations and attempts to slow down climate change were probably among the topics on which researchers concentrated.

Seven uses of the word "environment" show that it is a recurring subject. This implies maintaining a focus on environmental concerns and the interactions between human actions and the environment as a whole. It's likely that academics and researchers have examined numerous environmental concerns and their effects on various industries and regulations.

Six occurrences of "regional economy" are found in the literature. This shows a rise in interest in learning about the dynamics and growth of regional economies and how they relate to sustainable practices. The emphasis on regional economies indicates that scholars are looking at the regional and context-specific elements affecting sustainable development and economic progress.

Another subject with five mentions per topic is "crowdfunding". This shows that crowdsourcing is becoming more and more popular as a method of finance, especially for sustainable projects. Crowdfunding's ability to assist green projects, as well as the opportunities and challenges that come with it, has probably been studied by researchers.

With 631 occurrences, the topic "green finance" stands out due to its very high frequency and demonstrates its rising importance in the literature. This demonstrates a rise in interest in the nexus between finance and environmental sustainability. The methods, laws, and procedures that encourage financial investments in green projects and companies have probably been studied by academics and policymakers.

With 92 mentions, "China" stands out as being quite popular. In the context of green finance and sustainable development, this suggests a strong focus on China's participation. Researchers are probably looking at China's policies and initiatives and how they may affect international sustainability efforts.

The phrase "sustainable development" also comes up 70 times, demonstrating a steadfast interest in learning and implementing sustainable practices in a variety of fields. There is a good chance that academics have looked into the frameworks, policies, and tactics that help achieve long-term sustainable development goals.

Seventeen times are mentioned when the term "carbon neutrality" is brought up, which shows that efforts to achieve it are becoming more and more of a priority. To minimize greenhouse gas emissions and combat climate change, researchers have probably looked into a variety of strategies and regulations.

ESG (environmental, social, and governance) is a term with a frequency of ten references, which reflects the growing understanding of the significance of ESG aspects in investment choices and company practices. The incorporation of ESG factors into financial analysis and decision-making processes has probably been researched by researchers and practitioners.

Last but not least, the phrase "green finance policy" is used nine times, showing that policies that support and oversee green finance efforts are a particular emphasis of the study. It's likely that academics and policymakers have looked at how well these policies work and how they affect the growth of sustainable practices and investments.

In conclusion, study subjects that have attracted interest over time are shown by an analysis of trend topics in the bibliometric data. These themes show the continued attempts to understand and manage environmental concerns through research, policy, and finance, from global agreements like the Paris Agreement to specific topics like green finance and sustainable development.

Themes of green finance

This study uncovered a variety of topics relating to green finance as well as potential areas for further research. The descriptions of the themes are presented in Fig.  10 . Different themes related to green finance, along with significant studies that contributed significantly, are discussed below.

Green finance and environmental sustainability

In recent years, there has been a growing emphasis on the significance of green finance and environmental sustainability, leading to increased attention and focus in both academic research and practical applications. The world is currently experiencing an unparalleled environmental crisis, with issues like resource depletion, biodiversity loss, and climate change becoming more pressing. Green finance, which falls under the umbrella of sustainable finance, centers its attention on investments and financial methods that not only yield economic profits but also contribute to favorable environmental consequences.

Existing research mostly focuses on green finance and environmental sustainability in Asian countries, with specific focus on China. Green finance's function in low-carbon development has been thoroughly studied in relation to carbon emissions [ 13 , 147 ]. Green financing and renewable energy growth have also received attention, aiding China's clean energy revolution [ 4 , 12 , 20 , 21 , 40 , 49 , 51 , 56 , 61 , 67 , 72 , 75 , 76 , 80 , 85 , 89 , 97 , 104 , 105 , 107 , 109 , 110 , 119 , 121 , 129 , 135 , 144 , 145 , 149 , 169 , 172 ]. Environmental rules and green finance have also been studied to determine how well they promote sustainable financing [ 19 , 22 , 62 , 114 , 123 , 145 , 159 ].

When it comes to the study of regions outside of Asia, such as Africa, South America, and parts of Europe, there is a significant knowledge gap. It may be helpful to gain useful insights into regional variances and strategies if one is able to comprehend the various ways in which these various regions approach green financing and environmental sustainability initiatives.

Green finance and investments

Following a global shift toward sustainable and ecologically responsible economic practices, green finance and investments have developed dramatically.

Green bond quality and effectiveness, notably in China, is a major study topic. Green bonds finance ecologically friendly projects, therefore verifying their quality is crucial to green financial markets. To help green bonds meet sustainability goals, researchers have studied their quality procedures and standards [ 3 , 6 , 9 , 10 , 33 , 34 , 35 , 38 , 79 , 92 , 95 , 108 , 115 , 164 ]. The relationship between green and non-green investments is another frequent research topic. Researchers have studied the hedging or diversification impacts of these two assets. This study examines how green and non-green investments affect portfolio strategies, risk management, and the financial environment [ 1 , 116 ]. Another interesting relationship is natural resource richness, FDI, and regional eco-efficiency. Given global agreements like COP26, scholars are studying how natural resources and FDI effect regional ecological efficiency as states attempt to combine economic growth with environmental sustainability [ 15 , 36 , 42 , 143 , 157 ].

A key feature of green finance study is how financial institutions, integrate green investment and financing teams. The green finance agenda requires understanding how bank’s structure and behave to encourage sustainable investment. Green financial instrument creation and effect are another study topic. Researchers have examined green finance products including green bonds and minibonds to determine their performance and impact on environmental and sustainability goals. This field helps design policies and strategies to optimize industrial structures and promote sustainable development.

Green finance research examines how it affects industrial structures. Studies have examined how green finance initiatives including loans and investments optimize and shift industrial sectors toward sustainability. These findings are crucial for governments and business stakeholders seeking financial incentives for eco-friendly operations [ 12 , 31 , 46 , 57 , 85 , 96 , 124 , 130 , 139 ].

Green finance market interactions with financial variables must also be assessed for sustainable financial development. Researchers examine the relationship between green financial indices and other financial indicators to better understand how green finance affects the financial landscape [ 27 , 32 , 48 , 68 , 137 ].

Green finance and investments have many unexplored areas, presenting research opportunities. The behavioral dimensions of green investment focus on the psychological drivers and biases that influence investment choices; subnational and local initiatives, which are frequently ignored despite their crucial role in ecological action; cross-country comparisons to provide a more holistic view of effective green finance practices; the role and impact of green finance in emerging economies; and innovative green financial instruments like blockchain. Examining these lesser-known aspects could improve our understanding of sustainability in the financial sector and offer insightful information to investors, financial institutions, and legislators that want to make a positive impact on a more sustainable and environmentally friendly future.

Green finance and innovation

The convergence of green finance and innovation is a crucial topic that addresses the pressing global concerns of environmental sustainability and financial stability. Much study has been done on green finance and innovation, yet various themes and gaps emerge, demonstrating its complexity.

Green financing policies and instruments promote innovation, especially in environmental technologies and renewable energy. Many studies have studied how green funding affects green innovation and if it promotes sustainable technology. They've studied green bonds, green banking, and green finance reform laws, offering empirical evidence that financial incentives combined with green practices can stimulate environmental innovation [ 16 , 41 , 44 , 47 , 52 , 64 , 70 , 81 , 87 , 107 , 133 , 152 , 162 ].

The role of environmental legislation in green financing and innovation is another common theme. Researchers have studied how these restrictions affect green finance's impact on technology. Studying how financial policies and regulatory frameworks interact has helped explain the complex dynamics affecting innovation in environmentally sensitive industries [ 11 , 29 , 54 , 84 , 120 , 126 , 132 , 151 , 152 , 174 ].

Nevertheless, there are obvious gaps in the existing knowledge within the field. The effects of green finance on innovation have been extensively studied, but a better knowledge of the factors driving innovation in other areas is needed. Further study may reveal how green funding might boost innovation in non-environmental industries. How can financial mechanisms support sustainable transportation, agricultural, and urban planning innovation.

Further research is needed on education and the human element in green innovation. How green finance, educational investments, and innovation interact can help individuals, businesses, and societies develop a sustainable future. Green finance and innovation's impact on environmental adaptation and resilience also understudied. More research is needed to determine how financial mechanisms and new solutions may help communities and organizations adapt to climate change.

Green finance policy/green credit guidelines

Climate change and environmental degradation are major worldwide issues. Green finance, which promotes environmentally and socially responsible investments, is a key instrument in this battle. Research and discussion have focused on how green finance policies affect the economy and environment.

The switch to renewable energy is crucial to fighting climate change globally. This transition relies on green financing initiatives. Researchers are investigating how well such regulations promote renewable energy. They examined how green finance regulations affect renewable energy output, investment, and job development in this growing sector. Understanding these implications helps improve green finance initiatives for sustainability [ 18 , 98 , 118 ].

China and other nations have implemented green finance pilot programs to test the waters and stimulate innovation. This research evaluates pilot policy implementation and impacts. Scholars use synthetic control and other tools to study how these initiatives affect green innovation. The results help determine the real-world implications of such experiments and their potential for wider use [ 48 , 113 , 121 , 131 , 146 , 162 ].

Green financing policies vary worldwide. Comparative research of green financing rules can highlight policy differences among jurisdictions. Researchers compared the EU and Russia's green financing laws. These studies emphasize differences, similarities, and the potential influence of these policies on green finance development, promoting cross-border cooperation and knowledge exchange [ 60 , 125 ].

Monitoring and measuring green finance progress is essential for future development. Researchers are developing green finance indices to assess green finance in a country or region. These indices help policymakers, investors, and the public understand green finance's growth and potential [ 141 ].

Despite significant and informative research on green finance policies and their effects on the economy and environment, several research gaps and opportunities for additional investigation remain. First, a thorough evaluation of the durability and long-term sustainability of green finance policies is lacking in the literature. Many studies focus on short-term outcomes, but long-term planning and implementation need understanding these policies' long-term implications. Second, green finance policies' cross-border effects need greater study. As the global economy grows more interconnected, it's important to understand how regional policies affect others and the possibility for international collaboration. Green finance and social effects as creating employment and community development are understudied. Such studies could illuminate these policies' overall impact. Finally, additional multidisciplinary research combining economics, environmental science, and social science are needed to comprehend green finance policies' complex implications. Scholars can fill these gaps to improve our understanding of this crucial topic and inform sustainable policymaking.

Green finance and economy

The relationship between carbon intensity and economic development is a growing topic in green finance research. How nations may shift to low-carbon economies while maintaining economic growth has been studied. Several studies have quantified how green finance policies reduce carbon emissions and boost economic growth [ 63 , 71 , 122 , 155 , 175 ].

The study of the impact of green financing on agriculture, particularly in China, is gaining attention. Green financing impacts agricultural trade, sustainability, and food security, according to researchers [ 37 , 140 ]. Given its connection with economics, food production, and sustainability, this type of researches is crucial.

Efficient utilization of natural resources in Asian countries has gained attention for promoting green economic growth. Researchers have studied how nations might maximize economic gains from natural resources while reducing environmental harm. Addressing sustainable economic development concerns requires this area [ 86 , 101 , 146 , 166 ].

The significance of judicial quality in reducing emissions without hindering economic growth is a common issue in green finance research. Researchers examine how strong legal systems can enforce environmental laws and promote green practices while boosting the economy [ 154 ].

Even while the previously stated research topics have unquestionably enhanced our understanding of the intricacies of green finance, there are still a number of uncharted territories and research gaps that need to be investigated further. Currently, research on green finance mostly focuses on economic and environmental concerns. Integrated research combining economic, environmental, and social science is needed. It can provide a holistic view of green finance policy' many implications. The globalization of green finance policy has significant implications and cross-border effects. These policies' worldwide spillover effects and country collaboration are rarely studied. Research is lacking on how regional policies affect others and international cooperation.

Green finance and corporate social responsibility

Fostering CSR requires understanding how environmental regulations affect companies' sustainable strategies. Researchers should examine how CSR goals can be better aligned with regulations to improve environmental and social outcomes. Researchers have studied green finance-CSR approaches to promote sustainability. This research seeks to understand how green finance initiatives like green bonds and sustainable investment practices affect CSR performance [ 173 ]. Businesses and investors looking to maximize their environmental and social impact must understand these mechanisms.

One intriguing research topic is empirical evidence from heavily polluting enterprises, especially in China. This study shows how green finance can reduce environmental harm and promote CSR in industries with a high environmental impact [ 45 , 66 ]. Researchers can find ways to help heavily polluting companies become more sustainable by studying their experiences.

Bangladesh banks' CSR and green finance practices have also been studied [ 168 ]. This study studies how green financing affects financial institution CSR and environmental performance. Financial organizations can use these results to incorporate environmental responsibility while being profitable. Another relevant research topic is post-pandemic CSR practices as a business strategy to combat volatility and drive energy and environmental transition [ 53 ]. Understanding how CSR and green finance can help companies whether economic downturns and pandemics are crucial. This research can help businesses adapt to changing business conditions.

Further studies can explore socially responsible mutual funds and low-carbon economies. The impact of the investment industry on sustainability and environmental responsibility can be better understood by scholars by examining how these funds affect company behavior and investment decisions. Investors and businesses pursuing sustainable development may find these insights to be beneficial.

Green bond issuance is growing, thus study on its effects on company performance and CSR is needed. Investors seeking to support environmentally responsible businesses and companies contemplating green finance must have a comprehensive understanding of the repercussions on associated with green financing.

Trends/challenges/barriers/awareness of green finance

Regional patterns in China's green finance trends are well-studied, but little is known about applying these findings elsewhere, especially in countries with similar environmental issues [ 24 , 30 , 83 , 88 ]. Analysis of green finance growth by sector is common; however, there may be a knowledge vacuum about how sectors might learn from each other to create more successful sectoral plans [ 28 , 50 , 142 ].

Analyzing the structural barriers to green financing is vital, but also understanding how consumers, financial institutions, and governments can work together to close this gap is crucial. Political and institutional restrictions in green financing have been extensively examined, but cross-national comparisons might reveal similar concerns and inventive solutions. Cultural variety is crucial in ethical and green finance, but the challenges of adapting cultural methods to different places may not be adequately examined [ 7 ].

There were 213 papers pertaining to green finance research that were published between the years 2011 and 2021. However, between 2022 and May 2023, there was an enormous increase in the number of publications, which was 715. These publications can be found in Scopus and WoS. This spike can be associated with a number of causes that have encouraged both academia and industry to focus on sustainable and environmentally friendly practices. These drivers can be found in both the public and private sectors.

To begin, there has been a growing awareness of the urgent need to address climate change and its adverse impacts on the world. An increasing number of demands for action have accompanied this recognition. Green finance provides a means by which funds can be directed toward projects and investments that promote environmental sustainability, such as the development of sustainable infrastructure, clean technologies, and renewable sources of energy. In addition, global initiatives such as the Paris Agreement have put pressure on governments and financial institutions to align their strategies with climate goals, which has led to an increased demand for research on green finance practices and regulations [ 58 ]. Additionally, investors and consumers are becoming more aware of the environmental impact of their financial actions, which is contributing to an increase in demand for environmentally responsible investing products and services [ 39 ]. As a direct consequence of these developing tendencies, researchers and academics have developed responses to them, adding to the expanding body of literature on green finance.

993, more than any other nation, are references to China. This shows a keen interest in learning about China's economy, politics, and development. Researchers have concentrated on China's position in finance, sustainability, and innovation given its status as the world's largest population country and its growing global relevance due to its critical role in fostering sustainable and low-carbon development. Reduced energy use and waste are the goals of energy efficiency measures, which also have a positive effect on the environment by reducing greenhouse gas emissions. Researchers want to comprehend the procedures, regulations, and financial tools that can successfully encourage and support energy efficiency projects, which will ultimately contribute to a greener and more sustainable future. This is why they are focused on energy efficiency within the context of green finance [ 2 , 14 , 60 , 67 , 69 , 74 , 106 , 117 , 134 , 136 , 156 , 160 , 170 ].

The construction of pilot zones for green finance reform and innovations (GFRI) is a significant step the Chinese government has taken to build a green economy. Many authors have conducted surveys on China's GFRI policy and its impact on innovations. The GFRI policy program supports green innovation in large, polluting companies and urban green development by enhancing total factor productivity in pilot cities, emphasizing the importance of debt finance in corporate green innovation [ 40 , 82 , 148 , 150 , 153 , 158 ]. A different study by Wang et al. in 2022 [ 127 ] discovered that while the GFRP generally plays a positive role in fostering green technology innovation capabilities, the extent to which it has an impact varies depending on the region's resources, environment, and level of economic development, with middle- and high-income areas seeing a more noticeable impact. Wang et al. in 2022 [ 127 ] propose a green finance index, employing statistical indicators from 2011 to 2019, to analyze China's green finance development and predict its growth from 2020 to 2024. New energy, green mobility, and new energy vehicles have boosted China's green finance index during the previous nine years, according to research.

The Green Financial Reform and Innovation Pilot Zones (GFPZ) policy's effect on the ESG ratings of Chinese A-share listed firms between 2014 and 2020 is examined in another study. The findings showed that the GFPZ policy raises ESG scores, which are mainly based on social responsibility, and helps businesses in the pilot zones do better financially and environmentally [ 17 ]. In 2023, Shao and Huang [ 111 ] reviewed China's green finance policy mix, showing a shift toward market-based approaches and greater private sector engagement, influenced by dynamic vertical interactions between different levels of government.

Chen et al. [ 14 ] examined the response of China's equity funds to institutional pressure on green finance in 2021. The results showed that funds with negative screening strategies, which exclude environmentally harmful investments, have higher green investment levels and higher financial returns, while funds with positive screening strategies face negative investor reactions despite their green investments.

A study done by Lv et al. [ 88 ] found that while green finance development in China is improving, regional disparities and a polarization trend exist, requiring measures to narrow the gap and promote coordinated development across economic regions. Because it is crucial for striking a balance between economic development, environmental conservation, and social well-being, researchers in green finance concentrate on sustainability. The authors focused on studies on sustainable investment options, analyzed how environmental, social, and governance aspects are incorporated into financial decision-making, and evaluated how sustainability affects financial performance. Researchers are expected to advance ethical and sustainable financial practices and help the world accomplish its sustainability goals by studying sustainability within the context of green finance [ 5 , 25 , 43 , 46 , 59 , 73 , 77 , 90 , 91 , 94 , 104 , 109 , 138 , 161 , 163 , 165 , 167 , 171 ].

In conclusion, research on green finance has primarily focused on Asian countries, particularly China, where it plays a crucial role in low-carbon development and renewable energy growth. However, there is a significant knowledge gap in regions outside Asia, such as Africa, South America, and parts of Europe. Further research is needed to understand regional variances and strategies in these areas.

Studies have examined various aspects of green finance, including green bond quality, the relationship between green and non-green investments, and the impact of green finance on environmental and sustainability goals. Behavioral dimensions of green investment, subnational and local initiatives, cross-country comparisons, and the role of green finance in emerging economies have also been explored. Additionally, the role of green finance in stimulating innovation in environmental technologies and renewable energy has been studied, but there are gaps in understanding its impact on non-environmental industries and the human element in green innovation.

Further research is needed to understand the role of environmental legislation in green finance, its impact on technology, and its cross-border effects. The durability and long-term sustainability of green finance policies should also be examined, along with their social effects such as employment creation and community development. The relationship between carbon intensity and economic development, as well as the alignment of corporate social responsibility goals with environmental regulations, are important areas for investigation.

There is a need for more research on applying the findings from China's green finance trends to other countries facing similar environmental issues. Structural barriers to green financing should be analyzed, and the collaboration between consumers, financial institutions, and governments in closing this gap should be explored. Cultural diversity in ethical and green finance should also be considered, along with the challenges of adapting cultural methods to different places. Overall, further research in these areas can contribute to a more sustainable and environmentally friendly future.

When compared to other fields of study, it is clear that research on green finance has not been investigated to the same extent. In contrast to the less-researched areas of carbon, carbon emissions, climate change, financial systems, policymaking, agriculture, CSR, supply chain, risk management, corporate strategy, regional planning, and governance, green financing has been well-liked with investments, sustainable developments, green innovations, and green economies. On the other hand, taking into account the growing attention paid to sustainability on a worldwide scale and the pressing need to find solutions to the problems posed by the environment, it is quite likely that research into green finance will become more important in the years to come.

The increasing significance of sustainable development and the change to an economy with lower carbon emissions will require the development of innovative financial solutions to support green initiatives and assist the shift toward a financial system that is more friendly to the environment and more sustainable. It is anticipated that researchers will devote a greater amount of attention to green finance as the level of awareness regarding the environmental and social impacts of financial activities continues to rise. These researchers will investigate topics such as sustainable investment strategies, green bond markets, sustainable banking practices, and the incorporation of environmental considerations into financial decision-making. In addition to this, the incorporation of environmentally friendly financial practices into policy frameworks and regulatory measures further emphasizes the requirement for research in this particular area. In general, it is projected that research on green finance will pick up steam in the years to come because it plays such an important role in the process of sculpting a financially sustainable and resilient.

Availability of data and materials

SCOPUS and WoS databases.

Abbreviations

Corporate social responsibility

Financial Technology

Green finance reform and innovations

Green Financial Reform and Innovation Pilot Zones

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McKinsey Global Private Markets Review 2024: Private markets in a slower era

At a glance, macroeconomic challenges continued.

McKinsey Global Private Markets Review 2024: Private markets: A slower era

If 2022 was a tale of two halves, with robust fundraising and deal activity in the first six months followed by a slowdown in the second half, then 2023 might be considered a tale of one whole. Macroeconomic headwinds persisted throughout the year, with rising financing costs, and an uncertain growth outlook taking a toll on private markets. Full-year fundraising continued to decline from 2021’s lofty peak, weighed down by the “denominator effect” that persisted in part due to a less active deal market. Managers largely held onto assets to avoid selling in a lower-multiple environment, fueling an activity-dampening cycle in which distribution-starved limited partners (LPs) reined in new commitments.

About the authors

This article is a summary of a larger report, available as a PDF, that is a collaborative effort by Fredrik Dahlqvist , Alastair Green , Paul Maia, Alexandra Nee , David Quigley , Aditya Sanghvi , Connor Mangan, John Spivey, Rahel Schneider, and Brian Vickery , representing views from McKinsey’s Private Equity & Principal Investors Practice.

Performance in most private asset classes remained below historical averages for a second consecutive year. Decade-long tailwinds from low and falling interest rates and consistently expanding multiples seem to be things of the past. As private market managers look to boost performance in this new era of investing, a deeper focus on revenue growth and margin expansion will be needed now more than ever.

Perspectives on a slower era in private markets

Global fundraising contracted.

Fundraising fell 22 percent across private market asset classes globally to just over $1 trillion, as of year-end reported data—the lowest total since 2017. Fundraising in North America, a rare bright spot in 2022, declined in line with global totals, while in Europe, fundraising proved most resilient, falling just 3 percent. In Asia, fundraising fell precipitously and now sits 72 percent below the region’s 2018 peak.

Despite difficult fundraising conditions, headwinds did not affect all strategies or managers equally. Private equity (PE) buyout strategies posted their best fundraising year ever, and larger managers and vehicles also fared well, continuing the prior year’s trend toward greater fundraising concentration.

The numerator effect persisted

Despite a marked recovery in the denominator—the 1,000 largest US retirement funds grew 7 percent in the year ending September 2023, after falling 14 percent the prior year, for example 1 “U.S. retirement plans recover half of 2022 losses amid no-show recession,” Pensions and Investments , February 12, 2024. —many LPs remain overexposed to private markets relative to their target allocations. LPs started 2023 overweight: according to analysis from CEM Benchmarking, average allocations across PE, infrastructure, and real estate were at or above target allocations as of the beginning of the year. And the numerator grew throughout the year, as a lack of exits and rebounding valuations drove net asset values (NAVs) higher. While not all LPs strictly follow asset allocation targets, our analysis in partnership with global private markets firm StepStone Group suggests that an overallocation of just one percentage point can reduce planned commitments by as much as 10 to 12 percent per year for five years or more.

Despite these headwinds, recent surveys indicate that LPs remain broadly committed to private markets. In fact, the majority plan to maintain or increase allocations over the medium to long term.

Investors fled to known names and larger funds

Fundraising concentration reached its highest level in over a decade, as investors continued to shift new commitments in favor of the largest fund managers. The 25 most successful fundraisers collected 41 percent of aggregate commitments to closed-end funds (with the top five managers accounting for nearly half that total). Closed-end fundraising totals may understate the extent of concentration in the industry overall, as the largest managers also tend to be more successful in raising non-institutional capital.

While the largest funds grew even larger—the largest vehicles on record were raised in buyout, real estate, infrastructure, and private debt in 2023—smaller and newer funds struggled. Fewer than 1,700 funds of less than $1 billion were closed during the year, half as many as closed in 2022 and the fewest of any year since 2012. New manager formation also fell to the lowest level since 2012, with just 651 new firms launched in 2023.

Whether recent fundraising concentration and a spate of M&A activity signals the beginning of oft-rumored consolidation in the private markets remains uncertain, as a similar pattern developed in each of the last two fundraising downturns before giving way to renewed entrepreneurialism among general partners (GPs) and commitment diversification among LPs. Compared with how things played out in the last two downturns, perhaps this movie really is different, or perhaps we’re watching a trilogy reusing a familiar plotline.

Dry powder inventory spiked (again)

Private markets assets under management totaled $13.1 trillion as of June 30, 2023, and have grown nearly 20 percent per annum since 2018. Dry powder reserves—the amount of capital committed but not yet deployed—increased to $3.7 trillion, marking the ninth consecutive year of growth. Dry powder inventory—the amount of capital available to GPs expressed as a multiple of annual deployment—increased for the second consecutive year in PE, as new commitments continued to outpace deal activity. Inventory sat at 1.6 years in 2023, up markedly from the 0.9 years recorded at the end of 2021 but still within the historical range. NAV grew as well, largely driven by the reluctance of managers to exit positions and crystallize returns in a depressed multiple environment.

Private equity strategies diverged

Buyout and venture capital, the two largest PE sub-asset classes, charted wildly different courses over the past 18 months. Buyout notched its highest fundraising year ever in 2023, and its performance improved, with funds posting a (still paltry) 5 percent net internal rate of return through September 30. And although buyout deal volumes declined by 19 percent, 2023 was still the third-most-active year on record. In contrast, venture capital (VC) fundraising declined by nearly 60 percent, equaling its lowest total since 2015, and deal volume fell by 36 percent to the lowest level since 2019. VC funds returned –3 percent through September, posting negative returns for seven consecutive quarters. VC was the fastest-growing—as well as the highest-performing—PE strategy by a significant margin from 2010 to 2022, but investors appear to be reevaluating their approach in the current environment.

Private equity entry multiples contracted

PE buyout entry multiples declined by roughly one turn from 11.9 to 11.0 times EBITDA, slightly outpacing the decline in public market multiples (down from 12.1 to 11.3 times EBITDA), through the first nine months of 2023. For nearly a decade leading up to 2022, managers consistently sold assets into a higher-multiple environment than that in which they had bought those assets, providing a substantial performance tailwind for the industry. Nowhere has this been truer than in technology. After experiencing more than eight turns of multiple expansion from 2009 to 2021 (the most of any sector), technology multiples have declined by nearly three turns in the past two years, 50 percent more than in any other sector. Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later and exited in 2021 or before can be attributed to market multiple expansion and leverage. Now, with falling multiples and higher financing costs, revenue growth and margin expansion are taking center stage for GPs.

Real estate receded

Demand uncertainty, slowing rent growth, and elevated financing costs drove cap rates higher and made price discovery challenging, all of which weighed on deal volume, fundraising, and investment performance. Global closed-end fundraising declined 34 percent year over year, and funds returned −4 percent in the first nine months of the year, losing money for the first time since the 2007–08 global financial crisis. Capital shifted away from core and core-plus strategies as investors sought liquidity via redemptions in open-end vehicles, from which net outflows reached their highest level in at least two decades. Opportunistic strategies benefited from this shift, with investors focusing on capital appreciation over income generation in a market where alternative sources of yield have grown more attractive. Rising interest rates widened bid–ask spreads and impaired deal volume across food groups, including in what were formerly hot sectors: multifamily and industrial.

Private debt pays dividends

Debt again proved to be the most resilient private asset class against a turbulent market backdrop. Fundraising declined just 13 percent, largely driven by lower commitments to direct lending strategies, for which a slower PE deal environment has made capital deployment challenging. The asset class also posted the highest returns among all private asset classes through September 30. Many private debt securities are tied to floating rates, which enhance returns in a rising-rate environment. Thus far, managers appear to have successfully navigated the rising incidence of default and distress exhibited across the broader leveraged-lending market. Although direct lending deal volume declined from 2022, private lenders financed an all-time high 59 percent of leveraged buyout transactions last year and are now expanding into additional strategies to drive the next era of growth.

Infrastructure took a detour

After several years of robust growth and strong performance, infrastructure and natural resources fundraising declined by 53 percent to the lowest total since 2013. Supply-side timing is partially to blame: five of the seven largest infrastructure managers closed a flagship vehicle in 2021 or 2022, and none of those five held a final close last year. As in real estate, investors shied away from core and core-plus investments in a higher-yield environment. Yet there are reasons to believe infrastructure’s growth will bounce back. Limited partners (LPs) surveyed by McKinsey remain bullish on their deployment to the asset class, and at least a dozen vehicles targeting more than $10 billion were actively fundraising as of the end of 2023. Multiple recent acquisitions of large infrastructure GPs by global multi-asset-class managers also indicate marketwide conviction in the asset class’s potential.

Private markets still have work to do on diversity

Private markets firms are slowly improving their representation of females (up two percentage points over the prior year) and ethnic and racial minorities (up one percentage point). On some diversity metrics, including entry-level representation of women, private markets now compare favorably with corporate America. Yet broad-based parity remains elusive and too slow in the making. Ethnic, racial, and gender imbalances are particularly stark across more influential investing roles and senior positions. In fact, McKinsey’s research  reveals that at the current pace, it would take several decades for private markets firms to reach gender parity at senior levels. Increasing representation across all levels will require managers to take fresh approaches to hiring, retention, and promotion.

Artificial intelligence generating excitement

The transformative potential of generative AI was perhaps 2023’s hottest topic (beyond Taylor Swift). Private markets players are excited about the potential for the technology to optimize their approach to thesis generation, deal sourcing, investment due diligence, and portfolio performance, among other areas. While the technology is still nascent and few GPs can boast scaled implementations, pilot programs are already in flight across the industry, particularly within portfolio companies. Adoption seems nearly certain to accelerate throughout 2024.

Private markets in a slower era

If private markets investors entered 2023 hoping for a return to the heady days of 2021, they likely left the year disappointed. Many of the headwinds that emerged in the latter half of 2022 persisted throughout the year, pressuring fundraising, dealmaking, and performance. Inflation moderated somewhat over the course of the year but remained stubbornly elevated by recent historical standards. Interest rates started high and rose higher, increasing the cost of financing. A reinvigorated public equity market recovered most of 2022’s losses but did little to resolve the valuation uncertainty private market investors have faced for the past 18 months.

Within private markets, the denominator effect remained in play, despite the public market recovery, as the numerator continued to expand. An activity-dampening cycle emerged: higher cost of capital and lower multiples limited the ability or willingness of general partners (GPs) to exit positions; fewer exits, coupled with continuing capital calls, pushed LP allocations higher, thereby limiting their ability or willingness to make new commitments. These conditions weighed on managers’ ability to fundraise. Based on data reported as of year-end 2023, private markets fundraising fell 22 percent from the prior year to just over $1 trillion, the largest such drop since 2009 (Exhibit 1).

The impact of the fundraising environment was not felt equally among GPs. Continuing a trend that emerged in 2022, and consistent with prior downturns in fundraising, LPs favored larger vehicles and the scaled GPs that typically manage them. Smaller and newer managers struggled, and the number of sub–$1 billion vehicles and new firm launches each declined to its lowest level in more than a decade.

Despite the decline in fundraising, private markets assets under management (AUM) continued to grow, increasing 12 percent to $13.1 trillion as of June 30, 2023. 2023 fundraising was still the sixth-highest annual haul on record, pushing dry powder higher, while the slowdown in deal making limited distributions.

Investment performance across private market asset classes fell short of historical averages. Private equity (PE) got back in the black but generated the lowest annual performance in the past 15 years, excluding 2022. Closed-end real estate produced negative returns for the first time since 2009, as capitalization (cap) rates expanded across sectors and rent growth dissipated in formerly hot sectors, including multifamily and industrial. The performance of infrastructure funds was less than half of its long-term average and even further below the double-digit returns generated in 2021 and 2022. Private debt was the standout performer (if there was one), outperforming all other private asset classes and illustrating the asset class’s countercyclical appeal.

Private equity down but not out

Higher financing costs, lower multiples, and an uncertain macroeconomic environment created a challenging backdrop for private equity managers in 2023. Fundraising declined for the second year in a row, falling 15 percent to $649 billion, as LPs grappled with the denominator effect and a slowdown in distributions. Managers were on the fundraising trail longer to raise this capital: funds that closed in 2023 were open for a record-high average of 20.1 months, notably longer than 18.7 months in 2022 and 14.1 months in 2018. VC and growth equity strategies led the decline, dropping to their lowest level of cumulative capital raised since 2015. Fundraising in Asia fell for the fourth year of the last five, with the greatest decline in China.

Despite the difficult fundraising context, a subset of strategies and managers prevailed. Buyout managers collectively had their best fundraising year on record, raising more than $400 billion. Fundraising in Europe surged by more than 50 percent, resulting in the region’s biggest haul ever. The largest managers raised an outsized share of the total for a second consecutive year, making 2023 the most concentrated fundraising year of the last decade (Exhibit 2).

Despite the drop in aggregate fundraising, PE assets under management increased 8 percent to $8.2 trillion. Only a small part of this growth was performance driven: PE funds produced a net IRR of just 2.5 percent through September 30, 2023. Buyouts and growth equity generated positive returns, while VC lost money. PE performance, dating back to the beginning of 2022, remains negative, highlighting the difficulty of generating attractive investment returns in a higher interest rate and lower multiple environment. As PE managers devise value creation strategies to improve performance, their focus includes ensuring operating efficiency and profitability of their portfolio companies.

Deal activity volume and count fell sharply, by 21 percent and 24 percent, respectively, which continued the slower pace set in the second half of 2022. Sponsors largely opted to hold assets longer rather than lock in underwhelming returns. While higher financing costs and valuation mismatches weighed on overall deal activity, certain types of M&A gained share. Add-on deals, for example, accounted for a record 46 percent of total buyout deal volume last year.

Real estate recedes

For real estate, 2023 was a year of transition, characterized by a litany of new and familiar challenges. Pandemic-driven demand issues continued, while elevated financing costs, expanding cap rates, and valuation uncertainty weighed on commercial real estate deal volumes, fundraising, and investment performance.

Managers faced one of the toughest fundraising environments in many years. Global closed-end fundraising declined 34 percent to $125 billion. While fundraising challenges were widespread, they were not ubiquitous across strategies. Dollars continued to shift to large, multi-asset class platforms, with the top five managers accounting for 37 percent of aggregate closed-end real estate fundraising. In April, the largest real estate fund ever raised closed on a record $30 billion.

Capital shifted away from core and core-plus strategies as investors sought liquidity through redemptions in open-end vehicles and reduced gross contributions to the lowest level since 2009. Opportunistic strategies benefited from this shift, as investors turned their attention toward capital appreciation over income generation in a market where alternative sources of yield have grown more attractive.

In the United States, for instance, open-end funds, as represented by the National Council of Real Estate Investment Fiduciaries Fund Index—Open-End Equity (NFI-OE), recorded $13 billion in net outflows in 2023, reversing the trend of positive net inflows throughout the 2010s. The negative flows mainly reflected $9 billion in core outflows, with core-plus funds accounting for the remaining outflows, which reversed a 20-year run of net inflows.

As a result, the NAV in US open-end funds fell roughly 16 percent year over year. Meanwhile, global assets under management in closed-end funds reached a new peak of $1.7 trillion as of June 2023, growing 14 percent between June 2022 and June 2023.

Real estate underperformed historical averages in 2023, as previously high-performing multifamily and industrial sectors joined office in producing negative returns caused by slowing demand growth and cap rate expansion. Closed-end funds generated a pooled net IRR of −3.5 percent in the first nine months of 2023, losing money for the first time since the global financial crisis. The lone bright spot among major sectors was hospitality, which—thanks to a rush of postpandemic travel—returned 10.3 percent in 2023. 2 Based on NCREIFs NPI index. Hotels represent 1 percent of total properties in the index. As a whole, the average pooled lifetime net IRRs for closed-end real estate funds from 2011–20 vintages remained around historical levels (9.8 percent).

Global deal volume declined 47 percent in 2023 to reach a ten-year low of $650 billion, driven by widening bid–ask spreads amid valuation uncertainty and higher costs of financing (Exhibit 3). 3 CBRE, Real Capital Analytics Deal flow in the office sector remained depressed, partly as a result of continued uncertainty in the demand for space in a hybrid working world.

During a turbulent year for private markets, private debt was a relative bright spot, topping private markets asset classes in terms of fundraising growth, AUM growth, and performance.

Fundraising for private debt declined just 13 percent year over year, nearly ten percentage points less than the private markets overall. Despite the decline in fundraising, AUM surged 27 percent to $1.7 trillion. And private debt posted the highest investment returns of any private asset class through the first three quarters of 2023.

Private debt’s risk/return characteristics are well suited to the current environment. With interest rates at their highest in more than a decade, current yields in the asset class have grown more attractive on both an absolute and relative basis, particularly if higher rates sustain and put downward pressure on equity returns (Exhibit 4). The built-in security derived from debt’s privileged position in the capital structure, moreover, appeals to investors that are wary of market volatility and valuation uncertainty.

Direct lending continued to be the largest strategy in 2023, with fundraising for the mostly-senior-debt strategy accounting for almost half of the asset class’s total haul (despite declining from the previous year). Separately, mezzanine debt fundraising hit a new high, thanks to the closings of three of the largest funds ever raised in the strategy.

Over the longer term, growth in private debt has largely been driven by institutional investors rotating out of traditional fixed income in favor of private alternatives. Despite this growth in commitments, LPs remain underweight in this asset class relative to their targets. In fact, the allocation gap has only grown wider in recent years, a sharp contrast to other private asset classes, for which LPs’ current allocations exceed their targets on average. According to data from CEM Benchmarking, the private debt allocation gap now stands at 1.4 percent, which means that, in aggregate, investors must commit hundreds of billions in net new capital to the asset class just to reach current targets.

Private debt was not completely immune to the macroeconomic conditions last year, however. Fundraising declined for the second consecutive year and now sits 23 percent below 2021’s peak. Furthermore, though private lenders took share in 2023 from other capital sources, overall deal volumes also declined for the second year in a row. The drop was largely driven by a less active PE deal environment: private debt is predominantly used to finance PE-backed companies, though managers are increasingly diversifying their origination capabilities to include a broad new range of companies and asset types.

Infrastructure and natural resources take a detour

For infrastructure and natural resources fundraising, 2023 was an exceptionally challenging year. Aggregate capital raised declined 53 percent year over year to $82 billion, the lowest annual total since 2013. The size of the drop is particularly surprising in light of infrastructure’s recent momentum. The asset class had set fundraising records in four of the previous five years, and infrastructure is often considered an attractive investment in uncertain markets.

While there is little doubt that the broader fundraising headwinds discussed elsewhere in this report affected infrastructure and natural resources fundraising last year, dynamics specific to the asset class were at play as well. One issue was supply-side timing: nine of the ten largest infrastructure GPs did not close a flagship fund in 2023. Second was the migration of investor dollars away from core and core-plus investments, which have historically accounted for the bulk of infrastructure fundraising, in a higher rate environment.

The asset class had some notable bright spots last year. Fundraising for higher-returning opportunistic strategies more than doubled the prior year’s total (Exhibit 5). AUM grew 18 percent, reaching a new high of $1.5 trillion. Infrastructure funds returned a net IRR of 3.4 percent in 2023; this was below historical averages but still the second-best return among private asset classes. And as was the case in other asset classes, investors concentrated commitments in larger funds and managers in 2023, including in the largest infrastructure fund ever raised.

The outlook for the asset class, moreover, remains positive. Funds targeting a record amount of capital were in the market at year-end, providing a robust foundation for fundraising in 2024 and 2025. A recent spate of infrastructure GP acquisitions signal multi-asset managers’ long-term conviction in the asset class, despite short-term headwinds. Global megatrends like decarbonization and digitization, as well as revolutions in energy and mobility, have spurred new infrastructure investment opportunities around the world, particularly for value-oriented investors that are willing to take on more risk.

Private markets make measured progress in DEI

Diversity, equity, and inclusion (DEI) has become an important part of the fundraising, talent, and investing landscape for private market participants. Encouragingly, incremental progress has been made in recent years, including more diverse talent being brought to entry-level positions, investing roles, and investment committees. The scope of DEI metrics provided to institutional investors during fundraising has also increased in recent years: more than half of PE firms now provide data across investing teams, portfolio company boards, and portfolio company management (versus investment team data only). 4 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023.

In 2023, McKinsey surveyed 66 global private markets firms that collectively employ more than 60,000 people for the second annual State of diversity in global private markets report. 5 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023. The research offers insight into the representation of women and ethnic and racial minorities in private investing as of year-end 2022. In this chapter, we discuss where the numbers stand and how firms can bring a more diverse set of perspectives to the table.

The statistics indicate signs of modest advancement. Overall representation of women in private markets increased two percentage points to 35 percent, and ethnic and racial minorities increased one percentage point to 30 percent (Exhibit 6). Entry-level positions have nearly reached gender parity, with female representation at 48 percent. The share of women holding C-suite roles globally increased 3 percentage points, while the share of people from ethnic and racial minorities in investment committees increased 9 percentage points. There is growing evidence that external hiring is gradually helping close the diversity gap, especially at senior levels. For example, 33 percent of external hires at the managing director level were ethnic or racial minorities, higher than their existing representation level (19 percent).

Yet, the scope of the challenge remains substantial. Women and minorities continue to be underrepresented in senior positions and investing roles. They also experience uneven rates of progress due to lower promotion and higher attrition rates, particularly at smaller firms. Firms are also navigating an increasingly polarized workplace today, with additional scrutiny and a growing number of lawsuits against corporate diversity and inclusion programs, particularly in the US, which threatens to impact the industry’s pace of progress.

Fredrik Dahlqvist is a senior partner in McKinsey’s Stockholm office; Alastair Green  is a senior partner in the Washington, DC, office, where Paul Maia and Alexandra Nee  are partners; David Quigley  is a senior partner in the New York office, where Connor Mangan is an associate partner and Aditya Sanghvi  is a senior partner; Rahel Schneider is an associate partner in the Bay Area office; John Spivey is a partner in the Charlotte office; and Brian Vickery  is a partner in the Boston office.

The authors wish to thank Jonathan Christy, Louis Dufau, Vaibhav Gujral, Graham Healy-Day, Laura Johnson, Ryan Luby, Tripp Norton, Alastair Rami, Henri Torbey, and Alex Wolkomir for their contributions

The authors would also like to thank CEM Benchmarking and the StepStone Group for their partnership in this year's report.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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