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This paper investigates the associations among the Big 5 personality traits (neuroticism, conscientiousness, agreeableness, openness to experience and extroversion), nine prominent investment biases and the moderating role of financial literacy.

We used survey data from 475 individual investors in India based on various benchmarked scales in the literature and structural equation modeling to evaluate the desired relationship between the constructs.

Our evidence shows that the extroversion personality trait is the most vulnerable to behavioral biases, and overconfidence bias affects individual Indian investors the most. Financial literacy is positively associated with two biases (risk aversion and representativeness bias) and moderates the relationship between two personality traits (extroversion and agreeableness) and risk aversion.

Our study has limitations. First, it does not examine financial literacy in detail. Therefore, researchers should examine financial literacy within larger frameworks than those used in our study. Second, we confined our analysis to the Big 5 personality traits and nine behavioral biases. Our selection of biases to include in the study involved some subjectivity. Third, we limited our analysis to Indian investors. Researchers should replicate our study to see if its findings are generalizable in other countries with differing characteristics. Our findings call for a more careful examination of the circumstances behind which personality traits manifest in specific bias.

Investment advisors can help their clients make rational investment decisions by guiding them to deal with their investment biases.

Improving financial literacy could help investors avoid the pitfalls of behavioral biases and increase their performance in the stock market.

This study is the first to provide a comprehensive framework that examines the relationship between personality traits and investor biases and the moderating role of financially literate investors.

Financial technology (fintech) refers to using new technology to improve and automate the delivery and use of financial services. This chapter provides a brief introduction to fintech. It also includes the book's purpose, distinguishing features, intended audience, and structure. A synopsis of Chapters 2 through 23 is offered. The chapter concludes that fintech is constantly evolving and is reshaping finance. Fintechs offer a new paradigm of growth.

This chapter uses data from the Scopus database to present a comprehensive bibliometric analysis of fintech research, focusing on publication trends, citation patterns, and thematic clusters within the field. The analysis reveals notable trends, including influential publications, prolific authors, and their affiliations. It identifies and explores publications categorized into 23 distinct themes, representing key areas of inquiry in fintech, such as technological advancements, financial inclusion, innovation, data analytics, sustainability, and regulatory compliance. The study also identifies research gaps, indicating areas within fintech that have received limited scholarly attention. These findings provide valuable insights for researchers, policymakers, and industry practitioners. Researchers can better understand the fintech landscape, identify research gaps, and guide future inquiries. Policymakers can develop effective regulations and policies based on identified trends and challenges. Industry practitioners can leverage fintech developments to enhance their strategies and operations.

This chapter examines three common fintech use cases transforming the financial industry. First, the chapter introduces fintech's role in enhancing financial services and promoting financial inclusion, especially through digital platforms. Second, it investigates various fintech applications that support financial institution management by harnessing the power of artificial intelligence (AI) and machine learning (ML). Finally, the chapter explores fintech use cases related to the regulatory environment, including regulatory technology (regtech), blockchain technology, and cryptocurrencies. The insights presented in this chapter cater to researchers and practitioners keen on better understanding fintech's diverse applications in the ever-evolving financial industry landscape.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

For over a decade, fintech has challenged traditional business models and processes in the financial services industry. The ongoing disruption has necessitated the digital transformation of financial institutions (FIs) to remain an integral part of the financial system. This paradigm shift is not merely a technological update. Still, it signifies a cultural and operational rebirth, compelling FIs to embrace innovation, adaptability, and a customer-centric approach in the digital era. Independent of the business model, FIs must become digitally ambidextrous, offer tailored and dynamic customer experiences, support financial inclusion, and promote an environmental, social, and governance (ESG) agenda while leveraging data and remaining compliant. From digitalization to fintegration, the financial services industry's future is deemed to be an exciting and productive one.

This chapter discusses the contributions and challenges involving regulatory technology (regtech) in financial services. It explores the salient areas where regtech can and should focus, observing existing and forthcoming industry, technology, and legal developments. This chapter outlines regtech use cases to clarify the shaping of that industry sector. It draws on developments in industry and academia, where significant research sets the tone and direction of technological solutions and regulatory drivers. A brief critical account of the benefits and challenges in regtech is offered. This chapter presents potential future directions, focusing on the salient areas of environmental, social, and governance (ESG), cryptocurrency, and decentralized compliance.

We empirically investigate the impact of fintech innovation on dividend payout (DP) decisions. In addition, we also examine the mediated and moderated role of intellectual capital (IC) and board characteristics (BC) respectively in the fintech innovation-DP relationship.

Using a sample of 9,441 firm-year observations over the period 2014–2022, we develop a structural model that encompasses fintech innovation, IC, BC and DP decisions. We utilize fixed effects regression to empirically test the model. A battery of tests such as the two-step Generalized Method of Moment, Heckman’s two-stage selection correction and Difference-in-Difference regression are used to check the robustness and sensitivity of the estimates.

Our results suggest that fintech innovation significantly and positively impacts DP decisions and IC partially mediates the fintech innovation–DP relationship. In addition, BC such as independence, age and gender diversity are found to moderate this relationship.

This study’s originality lies in its micro-level analysis of the impact of fintech innovation on DP decisions, considering a novel firm-level innovation metric derived from patent applications. To our knowledge, no previous work has empirically examined the mediating role of IC and the moderating influence of BC in the fintech innovation–DP relationship, offering a unique perspective on the complex interactions shaping dividend policies in the digital era.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.