Trends and New Developments in FinTech

This Special Issue (Trends and New Developments in FinTech) discusses fintech trends such as the aspects of the regulation of digital activities, the implementation of technologies on reducing carbon emissions, ESG investments by FinTech, the trend towards asset tokenization and related banking activities in relation to FinTech, and the development of central bank digital currencies assisted by FinTech. This Special Issue (SI) comprises eight research papers and two reviews. Three themes highlight the research component of this SI—banking, FinTech technology, and financial assets. The two review papers discuss financial literacy and the use of Artificial Intelligence (AI) in finance. Below, I provide a brief summary of the papers in each theme, as well as the two reviews.

Bouheni et al., in Contribution 1, examined the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. This derived risk score can serve to mitigate the information asymmetry between the seller of receivables and the purchaser (funder). The authors found that their SURF Score is instrumental in justifying the seller–funder information asymmetry and provides high risk-appropriate periodic returns to the latter across industries.

Liu and Liang, in Contribution 2, assess whether FinTech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages. The authors estimate default probabilities using machine learning techniques (logit, random forest, and more) and found that non-FinTech lenders achieve the highest predictive accuracy, followed closely by banks, with FinTech lenders trailing. In pricing analysis, the authors reported that banks adjust the origination rates most sharply with borrower risk compared to FinTech and non-FinTech lenders.

Liu and Liang, in Contribution 5, investigated racial and ethnic disparities in mortgage lending outcomes across different lender types—large banks, FinTech lenders, non-bank lenders, small banks, and credit unions—using Home Mortgage Disclosure Act (HMDA) data from 2018 to 2023. The authors analyzed approval rates, rate spreads, and origination charges, subsequently evaluating how borrower outcomes vary by race and ethnicity, controlling for loan characteristics, borrower attributes, and regional factors. Their findings reveal that Black and Hispanic borrowers consistently face less-favorable terms than White borrowers, with disparities differing by lender type. In addition, although large banks demonstrate relatively equitable pricing, they impose higher loan denial rates on minorities. Credit unions, despite offering the lowest rates overall, penalize minority borrowers more severely in pricing than other lender types. FinTech lenders, while charging higher-rate spreads and fees, show smaller credit access disparities for minority borrowers. Non-bank and small bank lenders display mixed results, with inconsistencies in their treatment of minorities across pricing and credit access. These results suggest that in order to achieve equitable mortgage lending requires enhanced regulatory oversight, greater transparency in algorithmic decision-making, and the stricter enforcement of fair lending practices.

Finally, Zogning and Turcotte, in Contribution 7, examined the role of digital financial advisory services (robo-advisors), which are becoming increasingly popular in retail banking. These tools assist users with financial decisions such as risk assessment, portfolio selection, and rebalancing—all at a reduced cost. Building on the evidence that robo-advisors could complement human financial advisors, the authors evaluate robo-advisors’ effectiveness in asset allocation and their impact on retail banks’ profitability. Their findings indicate that implementing robo-advisors enhances profitability in non-interest activities, with this effect being more pronounced in France than in Canada.

Neverauskienė et al., in Contribution 3, studied Lithuanian financial technology (FinTech), which is one of the fastest-growing financial technology centers in Europe but also faces economic, regulatory, and technological challenges that hinder its development. The results revealed that factors such as favorable regulation influence the FinTech sector the most, which is crucial in attracting international investments. Based on the results, the authors recommend that authorities pay more attention to educational programs aimed at training technology specialists, promote cooperation between the public and private sectors, and further improve the regulatory environment to ensure the sustainable and safe development of FinTech.

On the FinTech and financial assets theme, Koutrouli and Manousopoulos, in Contribution 4, explore the use of crypto-assets for payments (selectively referring to stablecoins). Despite some of the financial and legal characteristics of crypto-assets, such as their price volatility and unclear legal settlement, rapid technological and regulatory developments in this area justify attention. We therefore try to answer the research questions of which, why, how, where, and by whom crypto-assets are used for (retail) payments. The authors conclude that fostering a clear understanding of the developments around crypto-asset payments and monitoring the various degrees of adoption throughout different markets could contribute to identifying the broader implications of using crypto-assets in the payment ecosystem and in maintaining the integrity and stability of the financial system.

Guo et al. investigate the key drivers and the economic and social impacts of cryptocurrency adoption, in Contribution 6. The authors found that technology development, measured by the Network Readiness Index, enables cryptocurrency adoption. Economic conditions, measured by higher national inflation rates and monetary policy indicators, are the key drivers for cryptocurrency adoption. Furthermore, cryptocurrency adoption has negative relationships with economic development, the unemployment rate, and social development. Finally, the authors reported that network readiness, economic conditions, and monetary policies contribute to fostering cryptocurrency adoption.

Contribution 8 in this category is by Sadorsky, who studied the practical implications of using precious metal ETFs to diversify risk in FinTech stocks. His analysis shows that gold provides the most downside risk protection. Downside risk reduction is estimated using relative risk ratios based on CVaR. In general, these results show the benefits of diversifying an investment in FinTech stocks with precious metals.

In the reviews category, Contribution 9 is by Croitoru et al., entitled “Exploring Financial Literacy in Higher Education with the Help of FinTech: A Bibliometric Analysis of Linkages to Access, Behavior, and Well-Being Through Digital Innovation”. The authors discuss the dynamic interaction between financial literacy and higher education within hundreds of articles (from the Web of Science) using bibliometric analysis. The authors identify four components of financial education—access and literacy, behavior, capability, and well-being—which reflect a spectrum of educational needs. Financial literacy also extends beyond knowledge to include behavior, health, and inclusion. Their findings underscore the multifaceted nature of financial literacy, linking it to access, behavior, and well-being. Given that financial education serves as the driver of behavioral change and societal resilience, the authors suggest that policymakers and educators should prioritize inclusive financial literacy programs that address demographic-specific needs, leveraging digital innovations to enhance accessibility and impact.

The second review paper—Contribution 10—entitled “A Comprehensive Review of Generative AI in Finance” by Lee et al., offers a comprehensive examination of recent trends and developments at the intersection of Generative AI (GAI) and finance. The authors address a number of questions such as what the current trends and advancements in the application of GAI within the financial sector are, how does GAI contribute to solving financial tasks and challenges, and what the risks and challenges associated with the use of GAI in finance are, as well as exploring how have these been addressed in the literature. The review paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies.