The Emerald Handbook of Fintech

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 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.

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 examines mobile payments and digital banking services. The past decade has seen a rapid increase in the use of alternative payment systems, away from cash to electronic payments. The digitalization of payments includes business-to-business (B2B), customer-to-business (C2B), and government-to-business and consumers (G2B/C), whether the payments are by computer, wire transfers, and point of sale (POS) systems. POS systems have become a standard in many retail outlets. Mobile payments use a smart device for contactless pay. Consumers see the increasing prevalence of payment systems when they go to the retail checkout or service counter. Worldwide, mobile payments are approaching 50% of digital. Digitalized payment systems are becoming more secure, decreasing concerns over mistaken payments, fraud, and errors. Consumers' confidence in value and usage decreases with age. Most fraud is due to scams and not hacking. Greater access to improved infrastructure and affordable smart devices will expand the usage of digitalized payment systems worldwide.

Marketplace lending has substantially changed since the first peer-to-peer lending platforms emerged in 2006. The industry is now an alternative to bank lending, predicted to total $70 billion for consumer and business loans worldwide by 2030. Marketplace lending is often deemed less safe than bank loans, mainly due to these portfolios' high degree of hidden information. These include needing more information on borrowers and potential correlations between them, which might lead to higher risk than is apparent at first glance. Deterministic processes cannot capture tail risk appropriately, so platforms and lenders should employ stochastic processes. This chapter introduces a Monte Carlo simulation and machine learning (ML) process to evaluate and monitor portfolios. For marketplace lending to become a viable and sustainable alternative to bank lending platforms, they must better evaluate, monitor, and manage tail risk in marketplace loans and develop tools to monitor and manage financial risk losses.

This chapter examines the world of risk management within fintech. It initiates by emphasizing the crucial role of technology and risk assessment in shaping the fintech landscape. It discusses various risk categories prevalent in fintech operations, elucidating the nuances of technology, operational, compliance, strategic, and reputational risks. A comparative analysis across different fintech sub-sectors unveils their distinct risk profiles. The narrative extends to proactive risk management frameworks, contrasting prominent models like the COSO ERM, FAIR Risk Quantification, and NIST Cybersecurity Frameworks. Integral defense measures are scrutinized, encompassing data encryption, access controls, vulnerability assessments, and incident response plans. This chapter underscores the significance of building operational resilience through robust technology infrastructure, regular system updates, disaster recovery planning, and business continuity measures. Ultimately, this chapter culminates in a comprehensive summary, offering pragmatic recommendations to fortify technology risk management in fintech.

Each company, large or small, starts with a dream and an idea for a new product or service. Companies can succeed or fail for a wide variety of reasons, including inexperienced managers, failure to build or sell the desired product, launching products into highly competitive environments, and a lack of capital. This chapter reviews the traditional methods of capital formation, including funding by angel investors and venture capital firms. These funding methods are only available to relatively large firms, leaving millions of small firms without reliable debt and equity funding sources to scale their business. The growth of the internet, blockchain technology, and fintech firms has introduced innovative funding methods, such as crowdfunding and Initial Coin Offerings (ICOs). While these structures have been successful in raising capital for smaller firms, changes in the regulatory environment, such as the JOBS Act, are needed for these new forms of capital formation to reach their full potential.

In recent years, investing with robo-advisors has gained momentum and is seen as a simplifying approach for individual investors to participate in financial markets. This chapter contributes to a better understanding of the concept of a robo-advisory and its implications for private investors by discussing its past, present, and future. It explores key issues, like cost-efficiency, historical performance, and automation levels, based on research and industry insights. Moreover, this chapter examines a robo-advisor's benefits, limitations, and challenges, like behavioral biases, regulation, and risk profiling. Finally, the importance of the ongoing megatrends of AI and green investing is examined concerning a robo-advisory.

This chapter discusses the evolution of online trading, its application in various market structures, and its benefits and potential concerns. Computers were first used in electronic communication networks among brokers and dealers to make trades and for informational purposes. Online brokers became popular with retail investors as the internet spread. Online trading comes with various trading protocols and order types. It enables traders to automate trading decisions and process data more easily using charting tools and customized programs connected to the broker's infrastructure. Electronic trading allows for greater centralization but can also be accompanied by market fragmentation. Market regulation has affected market structure and is still evolving. Centralization allows for more competitive prices and reduces search costs. Decentralized markets could cope better with asymmetric information.

The history of online trading began in the 1960s with the emergence of electronic communication networks, which allowed the electronic execution of trades outside traditional exchanges. The internet revolution led to the development of online brokerage platforms such as E*Trade and Schwab, enabling non-institutional investors to participate in the digital trading revolution. These platforms have evolved to serve the retail investor market, eventually adapting to mobile-first and commission-free models, significantly lowering the barriers to entry for financial markets. Platforms like Robinhood and other fintech firms have rapidly gained market share by offering services and products previously unavailable, such as commission-free trades, mobile trading, and novel products such as fractional shares and cryptocurrency investing. This chapter provides an overview of the history of online trading. It also introduces several new developments in fintech and the online trading industry and discusses various controversies and future implications of new technologies.

Fintech has revolutionized personal finance, introducing innovative tools that offer unprecedented access, efficiency, and security in managing finances. This chapter explains fintech's personal finance applications, from intuitive budgeting apps and advanced robo-advisors to peer-to-peer payment platforms. It articulates how these tools have shifted the control and management of finances into the hands of consumers, providing real-time financial data, customized investment strategies, improved credit scores, and streamlined transactions that eliminate the need for traditional intermediaries. Furthermore, this chapter features a select list of FinTech50 firms and highlights how individuals can leverage their services. This comprehensive guide is invaluable for individuals seeking to leverage fintech for personal finance optimization and for professionals keen on understanding and navigating the rapidly evolving fintech landscape.

Blockchain technology and its applications have recently received the attention of practitioners and academics. Visualizing the full impact these technologies will have on the world is challenging since their adoption is still in the early stages. This chapter explores how blockchain can disrupt the general business and financial world. Blockchain offers information transparency, live data synchronization, and immutable records that prevent fraud. Digital contracts and digital asset management may heavily depend on the development of blockchain and the adoption of smart contracts. Smart contracts can execute financial transactions automatically and enforce all parties' obligations without needing an intermediary and its cost. They can increase speed and simplify processes, reducing licensing ticketing costs and overhead charges. Blockchain also offers technology adoption solutions, like fair payment, cloud storage, smart contracts for financial assets, and international transactions with bilateral double taxation. It has further facilitated the creation of decentralized finance.

Cryptoassets are a diverse category of digital assets that rely on blockchain technology. They encompass various categories, such as cryptocurrencies, utility tokens, security tokens, tokenized assets and securities, and stablecoins. Cryptocurrencies are decentralized digital units of value that enable secure and transparent transactions. Utility tokens provide access to specific services or products within a blockchain network. Security tokens offer rights and entitlements similar to traditional securities, representing ownership in real-world assets or participation in investment opportunities. Tokenized assets and securities are digital representations of tangible or intangible assets, allowing for fractional ownership and enhanced liquidity. Stablecoins are blockchain-based digital assets designed to maintain a stable value, often pegged to fiat currencies or physical assets. This chapter examines each category's characteristics, benefits, and risks; explores their implementations and current applications in the fintech ecosystem; and discusses relevant regulations and future development opportunities.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

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.

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.

The insurance industry has been slow to adopt digital technologies due to high barriers to entry, product complexity, capital reserves, solvency requirements, and regulatory constraints. This chapter focuses on how insurtech is disrupting the insurance industry, resulting in a transformation from a traditional structure to a dynamic user-centric ecosystem. Next, it highlights the insurtech ecosystem by providing an in-depth analysis of the new paradigm on how insurtech is transitioning from the linear value chain to a more dynamic and interconnected value network. Finally, this chapter defines a perspective of insurtech's impact by identifying three waves of transformation within the insurance industry and understanding the evolution and chronology of insurtech's influence. Thus, this chapter provides insights into the opportunities and challenges of this technological breakthrough, offering a comprehensive view of insurtech's transformative journey within the insurance landscape.

The real estate industry has rapidly changed due to technological advances across residential and commercial real estate from the perspective of occupiers, investors, and service providers. Owners and buyers of properties have access to increasing information in the marketplace, including access to residential real estate platforms such as Zillow. Automated appraisals and artificial intelligence (AI) in the mortgage application process speed up home buying. Commercial real estate uses fintech to source deals, perform due diligence, and execute property management requests. This chapter includes a practitioner's view of the current and future information data needs, processes, and point solutions in the evolving technology landscape, including how tools such as ChatGPT apply. It concludes that the real estate fintech revolution has only begun, as data gaps in the real estate market require resolution before yielding better process automation and as the business model of real estate service providers shifts to strategic advisory roles.

In recent years, new and technologically innovative financial products and services, generally subsumed under the fintech umbrella, have permeated all areas of capital markets at an exponential rate. Primarily driven by developments in Web3 and advancements in artificial intelligence (AI), fintech solutions offer valuable benefits to all existing markets and participants and are the basis for introducing wholly new segments to classic capital market ecosystems. However, this increasing fintech adaptation does not come without challenges. Due to the technologies' nascent nature and often unregulated status, many products are susceptible to manipulation and fraud. The result can be sizable investor losses and excessive regulatory and public scrutiny. This chapter highlights the most essential and prominent fintech solutions used in capital markets today, along with their features, value additiveness, and degree of adaptation.

This chapter examines the patterns influencing the trajectory of fintech enterprises. With the looming challenge of climate change, the financial realm's responsibility in mitigating climate risks has surged into focus. This chapter investigates fintech enterprises' response to climate-related corporate social responsibility in six main domains: (1) climate risk assessment tools, (2) green bonds and sustainable investment tools, (3) ESG integration, (4) carbon trading and carbon credits, (5) sustainable banking, and (6) DeFi and climate initiatives. It also investigates how fintech firms recognize the impact of climate change within their official declarations and efforts to amplify consciousness about climate-related concerns. This chapter assesses climate-linked terminology and expressions using quantitative and qualitative approaches, illuminating these firms' dedication to assimilating climate risk within their operational blueprints.

This chapter explores the vast array of fintech opportunities. The industry commanded approximately $250 billion in revenue in 2022, which is predicted to grow to $1.5 trillion by 2030. Fintech firms are involved in everything from digital currencies to payment systems, lending platforms, and embedded finance. Firms use artificial intelligence (AI) and machine learning (ML) to create personalized financial products. One of the most important benefits to society is that fintech makes finance more inclusive to the traditionally underserved. However, fintech has its challenges. Regulations evolve, making compliance a challenge. Also, the industry is vulnerable to cyberattacks and money laundering. Companies hold large amounts of sensitive data, making them obvious targets for bad actors. As with many industries, governance, compliance, and transparency are essential for fintechs as they transform the financial services landscape.