FinTech

This study explores an innovative approach to incorporating option strategies into equity portfolios. It presents an alternative direction that institutional investors could take to overcome their current challenges, in a context where traditionally diversified portfolios of only equity and fixed-income assets have shown weaknesses that make it difficult for these investors to achieve their performance goals within their risk limits. We test whether a set of well-known backward-looking signals from equities markets and less-researched forward-looking ones from options markets can be used to improve the efficiency of two option strategies, namely covered call and protective put. The trend signal appears to be the one that adds the most value to both strategies. This study also shows that increasing complexity through additional trading rules does not improve the results of the more basic option strategies that make use of the signals. Full article

As healthcare systems face mounting pressure to modernise payment infrastructure, fintech innovations have emerged as potential tools to improve affordability and efficiency. However, the adoption of these technologies in clinical settings remains limited. This study investigated the perceptions and resistance patterns of healthcare professionals toward Buy-Now-Pay-Later technology and blockchain in healthcare finance, using Innovation Resistance Theory as the guiding framework. Survey data collected from medical practitioners (N = 366) were analysed to identify knowledge gaps, perceived risks, and tradition-related barriers that influence adoption intent. The findings reveal that while interest in financial innovation exists, resistance is driven by institutional conservatism, regulatory uncertainty, and limited familiarity with decentralised finance systems. This research contributes to the literature by offering a theory-based explanation for why even high-potential financial tools face behavioural and structural resistance in healthcare environments. Full article

This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012–2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve the highest predictive accuracy (AUC = 0.860), followed closely by banks (0.857), with fintech lenders trailing (0.852). In pricing analysis, banks adjust the origination rates most sharply with borrower risk (7.20 basis points per percentage-point increase in default probability) compared to fintech (4.18 bp) and non-fintech lenders (5.43 bp). Fintechs underprice 32% of high-risk loans, highlighting limited incentive alignment under GSE securitization structures. Expanding the allowable alternative data and modest risk-retention policies could enhance fintechs’ analytical effectiveness in mortgage markets. Full article

This study proposes a hybrid forecasting model that integrates the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a Long Short-Term Memory (LSTM) neural network to estimate Value at Risk (VaR) in the Rwandan foreign exchange market. The model is designed to capture both volatility clustering and temporal dependencies in daily exchange rate returns. Using daily data on USD, EUR, and GBP from 2012 to 2024, we evaluate the model’s performance relative to standalone GARCH(1,1) and LSTM models. Empirical results show that the hybrid model improves VaR estimation accuracy by up to 10%, especially during periods of elevated market volatility. These improvements are validated through MSE, MAE, and backtesting statistics. The enhanced accuracy is particularly relevant in emerging markets, where exchange rate dynamics are highly nonlinear and sensitive to external shocks. The proposed approach offers practical insights for financial institutions and regulators seeking to improve market risk assessment in emerging economies. Full article

There is an ongoing debate about the role of income diversification in enhancing bank stability within the financial services industry in Europe. Some advocate for diversification, while others argue that its importance should not be overstated. Some financial institutions are encouraged to focus on their traditional investments instead of income diversification, while others suggest that income diversification can stabilize or destabilize, depending on the regulatory environment. These conflicting results indicate a lack of clear evidence regarding the effectiveness of income diversification. Therefore, this paper aims to study the impact of income diversification on bank stability and enhance the predictive performance of bank stability by analyzing the period from 2000 to 2021 using a sample from 26 European countries, based on aggregate bank data. It employs a hybrid method that combines econometric techniques, specifically the generalized method of moments and a fixed-effects model, with machine-learning algorithms such as Random Forest and Support Vector Machine. These methods are applied to enhance the reliability and predictive power of the analysis by addressing the problem of endogeneity (via generalized method of moments) and capturing non-linearities, interactions, and high-dimensional patterns (via machine learning). The econometric findings reveal that income diversification can reduce non-performing loans, improve bank solvency, and enhance the Z-score, indicating the significant role of income diversification in improving bank stability. Conversely, the results also show that the machine-learning algorithms used play a crucial role in enhancing the predictive performance of bank stability. Full article

This paper studies the performance of ETFs that invest in companies involved in artificial intelligence (AI) technologies, such as firms focused on AI research, development, and applications. Using daily data from 15 American ETFs focused on AI-related companies over the period from 1 February 2019 to 29 December 2023, this paper investigates their investment style characteristics through a returns-based style analysis (RBSA). This study offers detailed insights into the degree of active versus passive management and highlights strategic patterns that may guide investment decisions in AI-themed financial products. We highlight that asset selection drives fund performances more than active management strategies, offering practical insights for investors and policymakers. Full article

The Lithuanian financial technology (referred to as FinTech) sector is one of the fastest-growing financial technology centers in Europe; however, this sector faces economic, regulatory, and technological challenges that hinder its development. This article aims to assess the state of development of Lithuania’s FinTech sector, identify the main challenges, and provide recommendations to promote the development of the sector. This study uses quantitative indicators, inter-criteria correlation, multi-criteria evaluation methods, and SWOT analysis. This article’s results will help identify the key factors that influence the growth of the FinTech sector in Lithuania and will be useful in shaping the sector’s further development strategy. The results of this study revealed that factors such as favorable regulation influence the FinTech sector in Lithuania the most, strengthening the innovation ecosystem and attracting international investments. However, the sector still faces challenges such as a lack of skilled labor, ensuring cybersecurity, and constant regulatory adaptation to new technologies. Based on the results of this study, it is recommended to pay more attention to educational programs aimed at training technology specialists, to promote cooperation between the public and private sectors, and to further improve the regulatory environment to ensure the sustainable and safe development of FinTech. Full article

Swiss banks are at a pivotal moment as digital assets gain traction, presenting both challenges and opportunities. This study examines how Swiss banks can leverage their internal resources and capabilities to establish a competitive advantage in the digital asset ecosystem. Using the Resource-Based View and the VRIO (Value, Rarity, Imitability, and Organization) framework, this study investigates the strategic importance of key services such as custody, staking, and tokenization. Drawing on expert interviews with Swiss banking leaders, this research identifies these services as vital for maintaining Switzerland’s financial leadership. Findings suggest that Swiss banks’ established reputation for trust, combined with regulatory clarity under the Distributed Ledger Technology Act, creates a strong foundation for digital asset adoption. While digital asset custody services address the growing demand for security, tokenization presents significant growth potential, particularly in real-world asset markets. This study concludes that Swiss banks can sustain their competitive edge by investing in blockchain expertise, fostering fintech partnerships, and enhancing educational initiatives. By combining traditional banking strengths with innovative digital asset services, Swiss banks are well positioned to capitalize on this evolving financial landscape. Full article

Financial technology (FinTech) rapidly transforms financial landscapes across ASEAN-4 countries by enhancing financial inclusion and digital service accessibility. However, the key factors driving FinTech development in these economies remain ambiguous. While existing studies highlight the economic and technological aspects of FinTech adoption, limited research distinguishes the unique conditions shaping FinTech’s evolution in developing ASEAN markets. This study bridges this gap by identifying economic and non-economic determinants and exploring their mediating effects. This research aims to investigate the primary drivers of FinTech development in ASEAN-4, emphasizing the roles of financial access and technological readiness as mediators in fostering a sustainable FinTech ecosystem. Utilizing structural equation modeling (SEM) with SmartPLS3, this study analyzes secondary data from 2008 to 2018, evaluating macroeconomic indicators, banking conditions, internet penetration, innovation levels, population dynamics, and human development factors. General banking conditions, access to finance, and technological readiness significantly impact FinTech development. Additionally, financial accessibility and technological infrastructure mediate the influence of economic stability, innovation, and digital penetration on FinTech growth. This study underscores policymakers’ and stakeholders’ need to enhance digital infrastructure and financial accessibility to accelerate FinTech growth. Strengthening financial ecosystems will drive digital transformation and economic resilience in emerging ASEAN economies. Full article

This study investigates how the rise of commission-free FinTech platforms and the introduction of fractional trading (FT) have altered trading behavior and order book dynamics in the NASDAQ equity market. Leveraging high-frequency ITCH data from highly capitalized stocks—AAPL, AMZN, GOOG, and TSLA—we analyze market microstructure changes surrounding the implementation of FT. Our empirical findings show a statistically significant increase in price levels, average tick sizes, and price volatility in the post-FinTech-FT period, alongside elevated price impact factors (PIFs), indicating steeper and less liquid limit order books. These shifts reflect greater participation by non-professional investors with limited order placement precision, contributing to noisier price discovery and heightened intraday risk. The altered liquidity landscape and increased volatility raise important questions about the resilience and informational efficiency of modern equity markets under democratized access. Our findings contribute to the growing literature on retail trading and provide actionable insights for market regulators and exchanges evaluating the design and oversight of evolving trading mechanisms. Full article

This paper explores the current use of crypto-assets for payments, focusing mostly on unbacked crypto-assets, while selectively referring to stablecoins. Although some specific characteristics of crypto-assets, such as their price volatility and unclear legal settlement, render them unsuitable for payments, the rapid technological and regulatory developments in the area of crypto-assets-based payments justify monitoring developments in this area. We therefore try to answer the research questions of which/why/how/where/by whom crypto-assets are used for (retail) payments. We analyse and describe a variety of ways in which crypto-assets are used for making payments, focusing on the period from 2019 to 2023 in Europe and worldwide, based on the publicly available statistical data and literature. We identify and exemplify the main use cases, payment methods, DeFi protocols, and payment gateways, and analyse payments with crypto-assets based on location and market participants. In addition, we describe and analyse the integration of crypto-assets into existing commercial payment services. Our work contributes to understanding the shifting domain of crypto-assets-based payments and provides insights into the monitoring of relevant developments via various dimensions that need to keep being explored, with the objective of contributing to the maintenance of the integrity and stability of the financial ecosystem. Full article

Initially designed as an automated ledger tool, Excel swiftly evolved into a data analytics platform for financial analysts to execute intricate financial analyses. Excel is so commonplace in the financial industry that many do not even consider it a fintech tool. The transformation of Excel from a simple ledger tool to a low-code machine learning (mL) platform is not a traditional focus for fintech. The transformation of Excel into an mL platform will let financial analysts and quantitative analyses quickly evolve financial models in Excel to use advanced mL techniques. The low-code interface lets analysts quickly build predictive models. This paper explores how Excel has evolved into a low-code machine platform for financial applications along with the risks associated with Excel’s new functionality. Full article

In the context of rapid advancements in financial technologies and the evolving demand of the digital economy, this study explores the transformative impact of FinTech and artificial intelligence (AI) on the banking sector, with a particular focus on payment systems. By examining innovative financial instruments and AI-driven solutions, this research investigates how these technologies enhance efficiency, security, and customer experience in banking operations. This study evaluates the integration of AI in payment systems, including its role in predictive analytics, fraud detection, and personalization, while aligning with global trends in digital transformation and sustainability. Adopting an interdisciplinary approach, this analysis highlights scalable and resilient strategies that address emerging challenges in the financial ecosystem. The findings provide a comprehensive framework for leveraging AI and FinTech to drive the evolution of banking services, supporting the transition toward a more innovative, digitalized, and sustainable financial future. Full article

This study examines the effect of FinTech on entrepreneurial performance and the essentiality of tax compliance and entrepreneurial orientation. Drawing on information from small and medium enterprises (SMEs) in Greece and utilizing Structural Equation Modeling techniques, our study shows that FinTech plays a key role in improving tax adherence and entrepreneurial mindsets, which subsequently enhances entrepreneurial success. FinTech promotes greater transparency, easier reporting, and less compliance burdens. Companies that make use of FinTech tools see enhancements in meeting tax regulation requirements efficiently and effectively without being weighed down by compliance issues that take up resources meant for innovation and strategic development instead. Moreover, this research highlights the impact of incorporating financial technology solutions for improved management and cultivating an innovative and forward-thinking environment. It highlights the importance of implementing strategies to boost FinTech adoption and foster entrepreneurial achievements, effectively sliding tax compliance into focus. Our research identifies the revolutionary impact of FinTech tools and sheds light on how technological progress can fuel entrepreneurship and improve business outcomes overall. Full article

The Markets in Crypto-Assets Regulation (MiCA) is a comprehensive European Union regulatory framework aimed at harmonizing the crypto-asset market. The existing literature has mainly examined MiCA from a legal perspective, while empirical assessments of industry perspectives remain scarce. In this study, we examine MiCA’s impact on the crypto market and its implications for both theory and practice by analyzing and integrating insights from 12 expert interviews. The findings reveal perceived benefits arising from the unified market, enhanced investor protection, and compliance clarity, alongside challenges related to the high regulatory burden, legal ambiguities, and limited innovation support. On this basis, we provide recommendations for improving the regulatory framework and its implementation. Furthermore, we integrate our findings within the technology–organization–environment (TOE) framework to provide a theory-based starting point for rigorous academic research. These findings contribute to regulatory discourse and offer practical guidance for the relevant stakeholders, including businesses, regulators, policymakers, and academics. Full article

The need for a sustainable economy has grown as technological advancements increasingly influence economic and social structures. This study investigates the role of FinTech and artificial intelligence (AI) in fostering sustainable development by facilitating green initiatives and promoting social responsibility. The research hypothesis posits that FinTech enables better access to financing for economic and social development projects, while AI enhances decision-making processes critical to the implementation of these initiatives. Through a qualitative approach, this study analyzes the interactions between FinTech, AI, and the Sustainable Development Goals (SDGs), exploring whether their relationship is bilateral or unidirectional. Using a quantitative approach, this study employs Principal Component Analysis (PCA) and Analysis of Variance (ANOVA) to examine the key factors influencing bank account ownership across different demographic groups and time periods. PCA is utilized to reduce data dimensionality while preserving the most significant variance, enabling the identification of underlying patterns in financial inclusion determinants. Meanwhile, ANOVA is applied to assess statistical differences in bank account ownership across demographic categories and the pre-pandemic, during-pandemic, and post-pandemic periods, highlighting the impact of digital financial services on financial inclusion trends in Europe. The findings suggest that both technologies play a significant role in supporting sustainability, with FinTech providing the necessary financial tools and AI optimizing decision-making. Furthermore, this study identifies barriers, such as regulatory challenges and technological gaps, that hinder the full integration of these technologies into sustainable development practices. It also highlights facilitators, such as policy support and technological innovation, that accelerate their adoption. The conclusions emphasize the transformative potential of FinTech and AI in achieving robust economic growth, reducing inequalities, and fostering a new cultural approach to resource management and societal responsibility. Full article

Inadequate financial literacy and overconfidence in financial knowledge, coupled with the use of mobile payments (MPs), may contribute to harmful financial behaviors. While the relationship between financial knowledge confidence and financial behaviors is well documented, there is limited understanding of how financial confidence affects the use of alternative financial services (AFSs), such as payday loans, and how MPs moderate this relationship. This study examines the moderating effect of MPs on the association between financial knowledge confidence and the demand for AFS, utilizing data from U.S. adults surveyed in the 2018 National Financial Capability Study. The results show that individuals who use MPs are significantly more likely to engage with AFSs compared to non-users, with MPs increasing the likelihood of AFS usage by 92% (odds ratio: 1.92). Furthermore, overconfident individuals who use MPs are 94% more likely to rely on AFSs (odds ratio: 1.94). These findings highlight the need for targeted financial education and policymaking to mitigate the risks associated with financial overconfidence and MP usage. Full article

This study examines 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. By analyzing approval rates, rate spreads, and origination charges, we evaluate how borrower outcomes vary by race and ethnicity, controlling for loan characteristics, borrower attributes, and regional factors. Our findings reveal that Black and Hispanic borrowers consistently face less favorable terms than White borrowers, with disparities differing by lender type. Large banks, operating under stringent regulatory oversight, demonstrate relatively equitable pricing but impose higher loan denial rates on minorities. Credit unions, despite offering the lowest rate spreads 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 banks display mixed results, with inconsistencies in their treatment of minorities across pricing and credit access. These results highlight that neither technological innovations nor alternative lending models alone suffice to eliminate systemic inequities. Achieving equitable mortgage lending requires enhanced regulatory oversight, greater transparency in algorithmic decision-making, and stricter enforcement of fair lending practices. Full article

This article delves into the transformative impact of blockchain technology on enhancing transaction quality and efficiency. Since the emergence of blockchain alongside Bitcoin in 2008, its decentralised and transparent nature has significantly improved transaction speed, security, and cost efficiency. These advancements have solidified blockchain as a foundational innovation in financial services. The paper examines critical milestones in blockchain, including Bitcoin, Ethereum, and Binance Coin (BNB), and their role in reshaping global finance by automating processes and reducing reliance on intermediaries. Additionally, the study evaluates blockchain’s impact on quality management, particularly emphasising how its immutable ledger system enhances the reliability and transparency of financial transactions. Despite challenges such as scalability, energy consumption, and regulatory hurdles, the potential for blockchain to redefine transaction quality in financial services is evident. This research contributes to the growing body of literature by integrating blockchain technology and traditional quality management systems, providing a comprehensive perspective on how the two domains influence one another. The findings underscore blockchain’s ability to drive innovation in financial services while addressing security, efficiency, and operational quality concerns. Full article