FinTech doi: 10.3390/fintech5020044

Authors: Zornitsa Yordanova Hamed Nozari

Startup investment decisions are always accompanied by high uncertainty, limited historical data, and the need to simultaneously consider financial performance, sustainability, and innovation. With the rapid expansion of financial technologies, the use of digital decision-support tools to manage this complex environment has become increasingly important. This study presents a multi-objective optimization framework for startup portfolio selection that simultaneously maximizes expected returns, minimizes downside risk using the Conditional Value-at-Risk (CVaR) measure, improves sustainability performance based on ESG indicators, and considers liquidity constraints. The main innovation of this study is the simultaneous integration of financial and non-financial criteria alongside a set of realistic structural constraints, including budget constraints, the number of options available, the concentration ceiling, and the minimum required levels for ESG, innovation, and liquidity. The results show that the proposed model is able to create a transparent balance between return, risk, sustainability, and investment horizon, and by changing the parameters related to risk and sustainability, it can target capital flows towards more innovative startups with higher ESG scores. This framework can be used as a practical tool for investors, digital investment platforms, and policymakers in responsible and data-driven capital allocation.

FinTech doi: 10.3390/fintech5020043

Authors: Paraskevi Gatzioufa Vaggelis Saprikis Georgios Avlogiaris Ioannis Antoniadis Konstantinos Panitsidis

As Artificial Intelligence (AI) becomes increasingly integrated into financial services, its alignment with sustainability goals has given rise to a new domain: Green FinTech. This study investigates the Behavioural Intention (BI) of Greek banking customers to adopt AI chatbots in the context of sustainable digital finance. Building upon the Unified Theory of Acceptance and Use of Technology (UTAUT), the proposed model incorporates additional constructs, i.e., Trust, Digital AI Literacy (DAIL), Environmental Concern (ENC), and Consumer Social Responsibility (CnSR), to examine the behavioural intention (BI) to use AI chatbots in the context of sustainable digital finance. Unlike prior UTAUT-based research, which has mainly examined AI, FinTech, or chatbot adoption separately or in different contexts, the present study develops and empirically tests an extended green-oriented UTAUT model that integrates technological, environmental, and ethical dimensions within a single framework. In this way, the study addresses a geographical, contextual, and model-specific gap in the literature, as research on AI chatbot adoption in Green FinTech remains limited, particularly in the Greek banking context. The target population for this study consists of educated, working-age adults who have already used an AI chatbot for a banking transaction in the context of e-banking services. A structured questionnaire was administered to a sample of 209 users of AI chatbots in the banking context. Using Structural Equation Modelling (SEM) and factor analysis via Principal Component Analysis (PCA) in conjunction with orthogonal rotation (VARIMAX), the results show that Green Performance Expectancy (GPE), Green Effort Expectancy (GEE), Digital AI Literacy (DAIL), and Trust significantly influence Behavioural Intention (BI). Consumer Social Responsibility (CnSR) also has an indirect impact via Green Social Influence (GSI). The study extends UTAUT in the Green FinTech context by integrating sustainability- and AI chatbot usage-related constructs, showing that Green Performance Expectancy and trust are the strongest drivers of bank customers’ behavioural intention to use AI chatbots. The study therefore contributes theoretically by extending UTAUT into a green-oriented framework that captures sustainability-related and ethical drivers of AI chatbot adoption in banking, rather than examining technology-use determinants alone. More specifically, it explains AI chatbot adoption in Green FinTech through a unified framework that combines core UTAUT variables with Trust, Digital AI Literacy, Environmental Concern, and Consumer Social Responsibility in the underexplored context of Greek banking.

FinTech doi: 10.3390/fintech5020042

Authors: Hamed Nozari Zornitsa Yordanova

This research presents an integrated framework for supply chain finance in which digital twin, blockchain, and multi-objective fuzzy optimization are used in synergy to improve financial decision-making in dynamic and uncertain environments. In this framework, the digital twin acts as a real-time monitoring and forecasting layer, blockchain acts as a trust and transparency infrastructure, and the optimization model acts as the decision-making core. To evaluate the proposed framework, a scenario-based mathematical model was developed and analyzed using a combination of real-world and simulated data. The results showed that the proposed framework was able to reduce the total cost by 18.6% and increase the return on investment to 12.4%. Also, the use of the digital twin framework significantly reduced financial risks and delays, while the integration of blockchain improved the transparency, traceability, and reliability of transactions and reduced operational errors. Overall, the findings show that this framework has high potential for developing smart, transparent, and resilient financial systems in the supply chain context.

FinTech doi: 10.3390/fintech5020041

Authors: Stefanos Balaskas

Artificial intelligence is increasingly embedded in consumer-facing FinTech, but trust in AI-enabled finance depends not only on performance, but also on whether users can understand and appropriately evaluate algorithmic outputs. This review synthesizes research on AI disclosure, explainability, and related transparency cues in consumer-facing FinTech, with particular attention to whether these cues support trust calibration rather than merely increasing trust or adoption. Searches in Scopus and Web of Science identified nine formally included studies and six adjacent contextual studies. The available evidence base is concentrated in robo-advisory and adjacent AI-enabled investment advising, with only limited evidence on automated credit decisions and crowdfunding recommendation platforms. The most studied cues are explanation/explainable AI and broader advisory or platform transparency, whereas disclosure, responsibility attribution, user control, and information-quality cues remain underexamined. Across the formal corpus, transparency cues are generally associated with more positive trust-related outcomes, especially trust and adoption-oriented responses. However, only a small subset of studies addresses trust calibration through outcomes such as reliance, fairness, accountability, and contestability. Overall, the current literature supports transparency more strongly as an acceptance mechanism than as a basis for appropriately bounded trust.

FinTech doi: 10.3390/fintech5020040

Authors: Shigeyuki Hamori

The rapid expansion of artificial intelligence (AI), particularly with the rise of generative AI technologies, has attracted increasing attention in financial markets. This study examines how the recent AI boom relates to changes in the interconnectedness of global financial markets. Using daily data from January 2021 to December 2025, we analyze spillover dynamics among AI-related equities, cryptocurrencies, and traditional financial assets within a time-varying parameter vector autoregression (TVP-VAR) framework. Our findings indicate that the emergence of generative AI is not associated with a uniform increase in financial connectedness. Instead, the overall level of connectedness declines modestly following the public release of ChatGPT by OPENAI in November 2022, while the structure of spillovers undergoes significant changes. In particular, AI-related equities initially act as net transmitters of shocks, but their relative importance diminishes over time. In contrast, broader equity markets, proxied by the S&P 500, remain the dominant source of spillovers throughout the sample period. These results are robust to alternative model specifications, including different lag lengths and forecast horizons. Overall, the findings suggest that the impact of AI on financial markets is better understood as a structural transformation of interconnectedness rather than a simple intensification of linkages. This study contributes to the literature by providing new evidence on how technological innovation reshapes financial spillover networks and highlights the importance of considering both the level and structure of connectedness in assessing systemic risk.

FinTech doi: 10.3390/fintech5020039

Authors: Otilia Manta Valentina Vasile Shigeyuki Hamori

The accelerated digitalization of financial systems, intensified by the strategic integration of artificial intelligence (AI), marks a profound paradigm shift in the global financial architecture [...]

FinTech doi: 10.3390/fintech5020038

Authors: Cristiano Wilson Carlos Tam

Background: Open banking (OB) is rapidly transforming financial ecosystems by enabling controlled data sharing among multiple actors through application programming interfaces (APIs). While this transformation promises innovation and competition, it also introduces complex security challenges that extend beyond purely technical considerations. Despite growing attention in academic and professional domains, existing reviews provide limited integration of security concerns with global adoption patterns and cross regional variation. Methods: This systematic review analyses empirical and conceptual research on security in OB published between 1999 and 2025, capturing early digital banking studies that later informed the development of OB. The literature is structured into three distinct phases: foundational digital banking developments, regulatory formalisation of OB frameworks, and post-implementation expansion of OB ecosystems. A comprehensive search was conducted across major academic databases and scholarly portals, complemented by relevant regulatory and policy sources. Following duplicate removal, title and abstract screening, full-text eligibility assessment, and methodological quality appraisal, 117 studies were retained for qualitative synthesis. Results: The findings reveal recurring security challenges arising from the interaction between technological infrastructures, regulatory frameworks, and user behaviour within OB ecosystems. Technical safeguards such as APIs, strong customer authentication, and encryption are necessary but insufficient when they are misaligned with regulatory implementation and user behaviour. Behavioural factors, including trust, consent understanding, and security-related decision making, play a central role in shaping ecosystem resilience. Based on this synthesis, the study develops a tri-dimensional security framework integrating technological, regulatory, and behavioural dimensions. The bibliometric analysis of 117 studies reveals that technological security dominates the literature (58%), followed by regulatory governance (44%) and behavioural dimensions (42%). However, only 17.9% of studies integrate all three dimensions simultaneously. APIs and authentication mechanisms represent the most frequent technological terms, while PSD2 and GDPR dominate regulatory discourse. Trust and decision-making are the most recurrent behavioural constructs. The relatively low proportion of fully integrated studies confirms a structural fragmentation within OB security research, thereby empirically justifying the proposed tri-dimensional framework. Chronologically, early studies (1999–2015) predominantly focused on technical security mechanisms and regulatory compliance, whereas more recent research (2020–2025) increasingly highlights the interplay between regulatory frameworks and user behaviour, suggesting a shift towards a more holistic understanding of security within OB adoption. Conclusions: This systematic review concludes that integrating technological, regulatory, and behavioural perspectives advances a more comprehensive understanding of security in OB ecosystems. The proposed tri-dimensional security framework provides a structured foundation for future research and supports policy-relevant and practice-oriented security design.

FinTech doi: 10.3390/fintech5020037

Authors: Kiryl Minkin Dariusz Drążkowski

Cryptocurrency adoption remains difficult to explain when treated as a single decision or static outcome. Addressing this limitation, the present study develops a qualitative, process-oriented account of cryptocurrency adoption among users in Central and Eastern Europe, with particular attention to how engagement emerges, changes, and stabilizes over time. Semi-structured individual in-depth interviews were conducted with 25 cryptocurrency users, and the material was analyzed using reflexive thematic analysis within an interpretivist framework. The findings show that adoption unfolds as a multi-phase process embedded in users’ biographies, financial practices, and socio-technical environments. Across accounts, cryptocurrencies were described not only as speculative assets but also as tools of financial autonomy, learning, and optionality under conditions of institutional uncertainty and constrained access to conventional financial pathways, making the CEE context particularly revealing for a process-oriented understanding of adoption. The analysis identified six interrelated themes: adoption as a project of financial autonomy; the “conscious investor” identity; the market as a school of cost and irreversibility; platforms and communities as adoption infrastructures; the relational politics of visibility; and practice stabilization. Together, these themes show that factors already highlighted in prior adoption research—such as trust, risk, autonomy, and knowledge—do not function as stable predictors, but change their meaning across different phases of engagement. The study contributes to FinTech adoption research by proposing a processual model that reconceptualizes cryptocurrency adoption as a phased, experience-dependent pattern of participation rather than a static outcome of parallel determinants. In doing so, it extends existing variable-centered frameworks toward a more dynamic and interpretive understanding of financial technology use.

FinTech doi: 10.3390/fintech5020036

Authors: Ding Ding Yang Li Poh Ling Neo Zhiyuan Wang Chongwu Xia

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets.

FinTech doi: 10.3390/fintech5020035

Authors: Mohammad Motasem Alrfai Mahmoud Khaled Al-Kofahi Ali Hasan Alkharabsheh Ibrahim Radwan Alnsour

Despite growing recognition of blockchain technology’s potential to enhance traceability, verifiability, and integrity in financial reporting, empirical evidence from regulated banking environments in developing economies remains scarce. This study investigates whether blockchain adoption is positively associated with transparency and accounting disclosure in digital financial reports among Jordanian listed banks. A structured questionnaire was distributed to managers, financial managers, and accountants across 15 banks listed on the Amman Stock Exchange, yielding 312 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5000 bootstrap subsamples was employed for data analysis. The results show that blockchain adoption is positively and significantly associated with transparency (β = 0.361, p < 0.001) and accounting disclosure (β = 0.437, p < 0.001), explaining 13.0% and 19.1% of the variance, respectively. These findings suggest that blockchain-enabled systems are perceived by banking professionals as contributing to greater reporting credibility. By providing empirical evidence from a developing economy banking sector, this study indicates that blockchain adoption may serve as a governance-supporting mechanism associated with improved perceived transparency and disclosure quality.

FinTech doi: 10.3390/fintech5020034

Authors: Hui Gong

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. The paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. It argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes.

FinTech doi: 10.3390/fintech5020033

Authors: Panagiotis Seitanidis Eleftherios Aggelopoulos Dimitrios Grypeos

This paper examines the factors associated with valuation patterns in FinTech and RegTech mergers and acquisitions (M&A) using a global sample of 3739 completed transactions sourced from S&P Global Market Intelligence from 2008 to 2025. We develop and empirically validate an integrated theoretical framework combining digital platform theory, open innovation theory, and control-based theories of the firm. We test our five hypotheses using semi-log regression models with heteroskedasticity-robust standard errors. We document five main findings. First, full acquisitions are associated with valuation premiums nearly three times larger than traditional M&A control premiums in baseline specifications, which remain economically large (~188%) after correcting for sample selection. Second, cross-border transactions are associated with significantly higher valuations. Third, infrastructure-oriented FinTech and RegTech segments are valued more highly than consumer-facing segments. Fourth, transaction values increase systematically over time, consistent with declining uncertainty as the sector matures. Fifth, deal structure explains more variation in transaction values than temporal or geographic factors, reversing conventional valuation patterns observed in financial-sector M&A. We further document that tighter financing conditions significantly depress valuations, though the underlying structural drivers of the FinTech premium remain robust to these macroeconomic shifts. Our findings contribute to the banking and finance literature by demonstrating that M&A in FinTech and RegTech exhibit a distinct valuation regime shaped by digital platforms and innovation-driven control mechanisms.

FinTech doi: 10.3390/fintech5020032

Authors: Zakia Siddiqui Claudio Andres Rivera

This empirical study examines how FinTech innovation is adopted, scaled, and sustained in a small and highly regulated market, such as Latvia. The triangulated analytical framework is applied in this study, integrating Rogers’ Innovation Diffusion Theory (IDT), De Meyer’s Innovation Ecosystem framework, and Value Chain Theory. This framework analyses the relationship between innovation characteristics, ecosystem relationships, and restructuring in the value chain. The data was collected from FinTech leaders, conventional financial institutions (banks), regulators, and associations, and was analysed thematically. Based on interviews with stakeholders, the relative advantage of Latvian FinTech lies in its flexibility, speed, and trialability; however, barriers to adoption result in complex regulation, an uneven distribution of technology in infrastructure, and differences in institutional readiness. The authors found strong collaboration among the ecosystem’s players but limited proactive regulatory engagement. This research provides a replicable model for cross-border or cross-sector analysis to assess the progress of innovation in regulatory and Environmental, Social and Governance (ESG) integration.

FinTech doi: 10.3390/fintech5020031

Authors: Konstantinos S. Skandalis Dimitra Skandali

The digital transformation of entrepreneurial finance has progressed beyond basic FinTech adoption toward the deeper digitalization of financial processes and the integration of artificial intelligence (AI). Yet, firms, particularly non-financial SMEs, vary substantially in their ability to convert these technologies into superior entrepreneurial, market, and financial outcomes. This study develops and tests a capability-based model explaining how FinTech-enabled financial process digitalization (FPD) and AI use shape entrepreneurship by influencing entrepreneurial performance outcomes. In line with current developments in digital finance, AI use is conceptualized as an embedded and complementary feature of FinTech-enabled financial process digitalization rather than an independent technological category. Drawing on the resource-based view and behavioral finance, we propose digital financial capability (DFC) as a central mechanism through which FinTech-enabled digitalized finance creates value, while credit fear is conceptualized as a behavioral constraint that limits entrepreneurial outcomes. We further posit customer satisfaction as a market-facing outcome linking financial capabilities to firm performance. Using survey data from 318 non-financial SMEs operating in Greece and applying Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings show that FPD and AI use significantly enhance DFC, which in turn increases customer satisfaction and entrepreneurial performance. In addition, financial process digitalization reduces credit fear, thereby mitigating its negative impact on entrepreneurial performance. By shifting the focus from technology adoption toward AI-supported capability development within digitally enabled financial processes and behavioral mechanisms, this study advances FinTech and entrepreneurship research and offers actionable insights for managers and policymakers seeking to leverage digital finance for sustainable entrepreneurial value creation.

FinTech doi: 10.3390/fintech5020030

Authors: Tifani Husna Siregar Adnan Ameen Bakather Emilios Galariotis

Saudi Arabia experienced rapid convergence in women’s financial inclusion between 2014 and 2024, a period marked by the 2018–2019 reforms expanding women’s economic rights and the accelerated deployment of digital payment infrastructure. Using four waves of Global Findex microdata (2014, 2017, 2021, and 2024), this study estimates probability-weighted logit models with average marginal effects and decomposes gender gaps using nonlinear Kitagawa and Blinder–Oaxaca methods. Reform-era dynamics are examined by tracing changes in the gender gap across survey waves. The findings indicate that aggregate gender gaps in account ownership and digital payment usage narrowed substantially by 2024, with conditional gaps among employed adults no longer statistically significant, while sizable disparities persist among individuals outside the workforce. Decomposition results highlight increased female labor force participation as a key correlate of convergence, consistent with labor market integration playing a central role in women’s financial inclusion during the reform era.

FinTech doi: 10.3390/fintech5020029

Authors: Aishwarya Malhotra Saiteja Puppala Eugene Pinsky

We examine a simple duration-rotation strategy applied to six U.S. Treasury ETFs spanning the full maturity spectrum, using data from 2007 to 2025. At each semi-annual rebalancing date, ETFs are ranked by prior-period return and divided into three equal groups—Winners, Median, and Losers. Contrary to conventional momentum logic, the middle group consistently outperforms. The Median strategy grows USD 100 to USD 199.90 by end-2025, a CAGR of 3.79% against 2.17% for the passive benchmark, with a higher Sharpe ratio (0.606 vs. 0.494) and a shallower maximum drawdown (−11.6% vs. −14.4%). Newey–West HAC and Lo (2002) tests confirm statistical significance (p=0.031 and p=0.014), and an expanding-window walk-forward procedure yields p=0.0005 across 27 out-of-sample evaluations from 2012 to 2025. The result is robust to calendar alignment, evaluation endpoint, lookback window, and execution timing, and survives transaction costs by a wide margin. The strategy requires no interest rate forecasts, no proprietary data, and is implementable with standard ETF brokerage access.

FinTech doi: 10.3390/fintech5020028

Authors: Oksana Liashenko Bogdan Adamyk Oksana Adamyk

This paper examines the market maturation hypothesis in cryptocurrency markets through a three-stage analysis of the evolution of tail risk in Bitcoin (BTC) and Ethereum (ETH). Using daily closing prices from January 2015 to February 2026 for BTC (n = 4058) and November 2017 to February 2026 for ETH (n = 3015), we employ 365-day rolling windows—reflecting the continuous 24/7 operation of cryptocurrency markets—to trace the temporal dynamics of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and Maximum Drawdown (MDD). The empirical strategy combines (i) Newey–West trend tests on rolling risk metrics, (ii) regime-conditional analysis across market states (Bull, Bear, or Neutral) and volatility regimes (high/low uncertainty), and (iii) exceedance correlation analysis to capture asymmetric BTC–ETH tail dependence. The results are consistent with the market maturation hypothesis: all ten trend coefficients across both assets are statistically significant (p < 0.001), with linear time trends explaining up to 46.8% (BTC VaR1%) and 67.5% (ETH VaR1%) of variation in rolling tail risk. Sub-period comparisons confirm economically meaningful declines—BTC VaR1% fell by 22.0% and ETH VaR1% by 26.6% between the early and late subsamples. However, maturation is markedly asymmetric across uncertainty regimes: tail-risk reductions concentrate in low-uncertainty periods, whereas BTC MDD in high-uncertainty regimes shows no significant improvement (+1.0%, p = 0.176). Excess correlation analysis reveals a persistent and widening downside asymmetry (ρ− = 0.847 vs. ρ+ = 0.246 at the 90th percentile), with late-period upper-tail correlation turning negative (ρ+ = −0.175 at the 95th percentile), implying that portfolio diversification within the cryptocurrency asset class remains illusory during market stress. These findings carry direct implications for institutional risk management, stress-testing frameworks, and prudential regulation of digital assets.

FinTech doi: 10.3390/fintech5020027

Authors: Zsuzsanna Deák Ádám Béla Horváth

This paper explores the relationship between risk-taking attitudes, different dimensions of trust, and the adoption of financial technology (FinTech) in an emerging Central European economy. Based on survey data collected via LimeSurvey (October to December 2025) in Hungary, multivariate linear regression models were estimated to explore the relationship between FinTech usage, individual risk-taking propensity, and four dimensions of trust, while controlling for socioeconomic variables. The results indicate that higher institutional trust in independent financial actors facilitates FinTech adoption. However, higher institutional trust in domestic financial and governmental actors has an inhibiting effect. When trust dimensions are added to the model, the positive association with general risk-taking propensity becomes statistically marginal, indicating that trust-related factors account for a substantial share of the observed variation. Further tests regarding the possible direction of this causation confirm that FinTech use is also linked to increased trust in independent financial actors. This study adds to the FinTech literature by demonstrating that usage is related not only to generalized trust and individual risk propensity but also to differentiated institutional trust attitudes. The findings highlight that institutional background is an important determinant of digital financial behavior in emerging economies.

FinTech doi: 10.3390/fintech5010026

Authors: Alberto Castillo Jose Manuel Mira Mcwilliams

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to 2023, we apply several established statistical techniques—including unit root tests, variance ratio analysis, Hurst exponent estimation, and GARCH modeling—to quantify the presence and strength of mean reversion in volatility. To assess the accuracy and practical usability of volatility metrics for option valuation, we compare realized volatility, GARCH-based forecasts, range-based estimators, and widely used implied volatility measures such as the VIX and daily implied volatility averages, benchmarking each against contract-specific implied volatility. The results indicate that more than 65% of the analyzed tickers exhibit statistically significant mean-reverting behavior, and that the 30-day average implied volatility consistently provides the most reliable predictive performance among the tested metrics, while range-based estimators perform poorly when applied to end-of-day data. Finally, backtests of six delta-neutral option strategies informed by these findings did not yield consistent profitability or statistically significant outperformance, suggesting that although volatility mean reversion is measurable, its direct application to systematic trading remains challenging.

FinTech doi: 10.3390/fintech5010025

Authors: Hela Mzoughi Arafet Farroukh Martina Metzger

This study investigates how financial technology (FinTech) contributes to economic growth, focusing on whether it acts primarily as a mediator or a moderator within the finance–growth nexus. A composite FinTech index is constructed using Principal Component Analysis based on cross-country data for 2021, and the analysis distinguishes between High-Income and Non-High-Income economies following the World Bank classification. The results show that in developing and emerging economies, FinTech mainly serves as a mediator, helping to close structural gaps in financial intermediation and expanding access to financial services. In High-Income countries, by contrast, FinTech acts as a moderator, enhancing innovation and efficiency in mature financial systems. When financial inclusion is disaggregated by gender, the findings reveal additional nuances. FinTech fosters growth through inclusion for both men and women, but its effects are stronger for male account ownership in developing economies and more balanced in High-Income contexts. In general, the study contributes to the literature by developing a multidimensional FinTech index, clarifying its dual mediating and moderating functions, and introducing a gender-sensitive perspective that highlights the uneven distribution of FinTech’s growth benefits between income levels and genders.

FinTech doi: 10.3390/fintech5010024

Authors: Lorenzo Barisone Edoardo Beretta Robert Bregy Vincenzo Carbone Roberto Gorini Giacomo Zucco

The present article examines the pioneering case of blockchain adoption in local government by the City of Lugano and discusses how Distributed Ledger Technology (DLT) may support institutional innovation beyond pilot experimentation. The Swiss municipality of Lugano has developed an integrated strategy that combines permissioned blockchain infrastructure (SwissLedger), a municipal payment token (LVGA), digital literacy and payment innovation initiatives (Plan ₿), and the issuance of fully digital municipal bonds. By adopting a case study methodology, the analysis draws on quantitative indicators of platform usage, operational data, and a sentiment analysis of media coverage to document technological developments and socio-economic patterns correlated with the initiative. SwissLedger has been adopted as an infrastructural experiment for secure document notarization, public administration digital services, open-finance interoperability with optional compliance tools, and sector-specific applications. Furthermore, the Plan ₿ initiative emerges as a communication catalyst, generating international visibility and positive sentiment, alongside descriptive statistics consistent with local economic activity. Lugano’s digital bond issuances also attracted attention to the potential of how DLT could support settlement processes and transparency in public finance. Overall, the evidence gathered suggests that DLT adoption in local government is not merely a technological upgrade, but rather part of a broader organizational transformation process. The case findings also outline a set of potentially transferable elements for municipalities seeking to align innovation with public value creation.

FinTech doi: 10.3390/fintech5010023

Authors: Lamprini Konsta Dimitrios Dimitriou Anastasios Papathanasiou Vasiliki Liagkou

This study empirically investigates the performance of Elastic Machine Learning, an industrial, unsupervised anomaly detection tool, in the identification of fraudulent behavior in banking transactions. Using AI-generated datasets that were designed to simulate realistic banking environments, the analysis examines three distinct fraud-related scenarios: (i) abnormal associations between a single account and multiple IP addresses, (ii) bursts of cross-border transactions within short time windows, and (iii) unusually high transaction values relative to historical behavior. The results show that the Elastic platform consistently detects anomalous patterns across all examined scenarios by flagging suspicious behavior during the fraud window in real time. This study provides the first empirical assessment of the operational behavior of an industrial, unsupervised anomaly detection platform across multiple fraud-related scenarios in the banking sector, offering practical insights for real-time fraud monitoring and early-warning systems, while supporting institutional resilience and the robustness of the financial system.

FinTech doi: 10.3390/fintech5010022

Authors: Stefanos Balaskas

Green FinTech involves facilitating sustainable payments, banking, and investment; nevertheless, it is subject to consumer trust and perceptions of ‘green’ value. The literature on this topic is fragmented, with information systems literature typically considering trust as a broad acceptance construct, while sustainable literature considers it as a risk of ‘greenwashing’ without integrating credibility into adoption models. This systematic review aggregates 15 empirical studies and addresses five research questions. RQ1 examines the theoretical models applied to examine trust in green/sustainable FinTech adoption. RQ2 examines the conceptualization and measurement of trust across different contexts, distinguishing institutional/provider trust, platform/tech trust, and sustainability claim credibility trust. RQ3 examines the function of trust within behavioral models (predictor, mediator, moderator). RQ4 examines methodological characteristics and quality indicators (research design, sampling frame, reliability, and bias). RQ5 examines the direct relationship between trust and adoption intention using meta-analysis. The systematic review follows a set of PRISMA guidelines, where we searched Scopus and Web of Science (2015–2026) and applied an RQ-based coding scheme to peer-reviewed articles. Measures of trust varied significantly (unidimensional, integrity–competence–benevolence, and technology-specific scales), limiting cross-study comparability. Using random effects, we found a significant positive relationship between trust and intention (pooled standardized direct path coefficient β = 0.27, 95% CI [0.14, 0.41]) with considerable heterogeneity (I2 = 88%) and a wide prediction interval including near-zero effects. Literature essentially endorses trust as a significant yet context-dependent construct, emphasizing the necessity for measurement standardization, a more distinct differentiation between sustainability trust and general platform trust, regular reporting of reliability and bias assessments, and focused evaluations of boundary conditions (e.g., environmental skepticism, regulatory framework, and FinTech type).

FinTech doi: 10.3390/fintech5010021

Authors: Andrey Vlasov Andrey Egorov Alexander M. Karminsky

Amidst the escalating geopolitical fragmentation of the global financial system, divergent stablecoin architectures are emerging. This study employs Qualitative Comparative Analysis (QCA) and introduces a formalized ‘Geopolitical Stablecoin’ (GPSC) model to conduct a systematic comparison of three representative cases: A quasi-sovereign asset within a coordinated closed-loop system, a commercial asset with global open-market circulation, and a state-issued asset representing a failed local initiative. Our analysis reveals that in the model implemented as a quasi-sovereign asset, parameters traditionally viewed as vulnerabilities—such as reserve opacity and a high degree of centralization—are functionally reinterpreted as elements ensuring its operational resilience. In contrast, the risks associated with the commercial asset model are emergent properties of its scale and decentralized distribution. The findings highlight the necessity for a differentiated regulatory approach aimed at targeted intervention in key architectural components of the model rather than the use of universal bans.

FinTech doi: 10.3390/fintech5010020

Authors: Wil Martens

As cryptocurrencies evolve from niche assets to systemic financial components, the banking sector faces a strategic dilemma: displacement or adaptation. Using 27,510 bank–year observations from 2014 to 2023 across thirty-two economies, predominantly within the European banking sector, this study isolates the technological prerequisites for this adaptation. We employ a continuous interaction model with robust controls to test how national digital infrastructure moderates bank responses to valuation cycles in the four dominant cryptocurrencies by market capitalization (Bitcoin, Ethereum, Ripple, and Binance Coin). The results document a robust lagged complementarity effect: in digitally advanced economies, cryptocurrency booms significantly increase bank non-interest income in the subsequent year, while lending portfolios remain unaffected. A one-standard-deviation increase in crypto returns interacts with digital capacity to boost fee revenue by approximately 0.7 percentage points (0.20 standard deviations). Crucially, this effect persists after controlling for GDP and equity market interactions, confirming that technological capacity, rather than general economic wealth, acts as the binding constraint. These findings refine FinTech adaptation research by demonstrating that high-bandwidth infrastructure enables banks to monetize external volatility via service deployment and custody, transforming a potential threat into a structural revenue stream.m.

FinTech doi: 10.3390/fintech5010019

Authors: Muhammad Ashfaq Maximilian Pfeifer Tan Gürpinar Mehmet Akif Gulum

This paper examines the relationship between traditional gold (XAU) and its tokenized counterparts (PAXG and XAUT), providing an empirical assessment of how digital representations of real-world assets align with their underlying benchmarks. Using multi-year time series data, the study evaluates price deviations, tracking accuracy, correlations, and volatility across both weekday-only and 24/7 trading datasets, incorporating weekend effects and crypto-market microstructure. Results show that both tokenized assets exhibit strong long-term alignment with XAU, while short-term divergences arise from continuous crypto trading, liquidity fragmentation, and issuer-specific design features, with XAUT consistently tracking spot gold more closely than PAXG. Building on this analysis, the paper examines the role of tokenized gold within dynamic, smart contract-driven crypto portfolios that also include BTC, ETH, and cash. Portfolio simulations demonstrate that adaptive rebalancing strategies materially improve risk-adjusted performance, with XAUT serving as a stabilizing anchor and cash enabling rapid, automated repositioning during volatility spikes. The findings offer a dual contribution: they clarify the fidelity and market behavior of tokenized gold and provide evidence of its practical utility within automated, on-chain portfolio management, highlighting both its strengths and structural limitations in emerging digital financial systems.

FinTech doi: 10.3390/fintech5010018

Authors: Gaurango Banerjee Shekar Shetty

This study examines the impact of corporate governance on sustainability-related risk in Indian banks across crisis and post-crisis periods. Using data from 37 public and private banks between 2006 and 2018, it analyzes how board characteristics influence liquidity and solvency risk. Panel regressions and a decision tree-based machine learning approach reveal consistent results: director busyness is associated with higher liquidity risk, while higher director and auditor fees are linked to improved liquidity management. Smaller, more independent boards and higher director fees are associated with lower solvency risk. The findings contribute emerging-market evidence on the governance–risk nexus and offer policy implications for bank governance and financial stability.

FinTech doi: 10.3390/fintech5010017

Authors: Karim Farag Loubna Ali Mohamed A. Hamada

Nowadays, the European economy faces significant global challenges that threaten the continuity of economic growth, especially in the German manufacturing sector, which is under strain from financial turmoil, resulting in numerous layoffs and firm closures. In this respect, FinTech significantly contributes to addressing these issues by providing data-driven analytical tools that improve the assessment and monitoring of firms’ financial position. However, in the literature, we have not found any paper that uses machine learning (ML) algorithms to assess the financial distress of German manufacturing firms, highlighting methodological and sectoral gaps that need to be bridged. Therefore, this study aims to develop an econometric and ML-based financial distress scoring model for German manufacturing firms by estimating contemporaneous Altman Z-scores that provide better insights into the financial distress determinants, enabling better financial management. The econometric findings revealed that the regression model has an adjusted R-squared value of 86%, confirming that the selected firm-specific and macroeconomic factors play a substantial role in explaining financial distress. The findings recommend that German manufacturing businesses retain more earnings rather than distributing them as dividends, while reducing their debt in capital structures to enhance financial stability. Moreover, the ML results found that Gradient Boosting and Random Forest have the highest accuracy scores among the ML methods, suggesting that these models provide strong capability for assessing financial distress and supporting more effective financial risk management, allowing firms to effectively respond to the threats of a dynamic environment and thereby better support the growth of the German and European economies.

FinTech doi: 10.3390/fintech5010016

Authors: Colin Kuka Sanar Muhyaddin Phoey Lee Teh Leanne Davies

Quantum computing threatens the security foundations of global financial systems, exposing long-lived data and signed digital assets to “harvest-now, decrypt-later” attacks. While the timeline for cryptographically relevant quantum computers remains uncertain, regulatory signals from the USA, UK, EU, Canada, and Australia converge: financial institutions and payment infrastructures must begin migrating to post-quantum cryptography (PQC) now to preserve confidentiality, integrity, and systemic stability. This paper maps emerging standards and roadmaps, contrasting binding requirements like the EU’s DORA crypto-agility provisions with non-binding guidance from NIST, ENISA, and ETSI. Despite a shared intent to secure high-risk use cases by 2030–2031 and complete migration by 2035, divergences in enforcement and milestones create uncertainty for cross-border banks and financial market infrastructures. In parallel, technical adoption is advancing: major browsers, cryptographic libraries (OpenSSL/BoringSSL), and CDNs (e.g., AWS CloudFront) have deployed hybrid PQC key exchange in TLS 1.3, proving confidentiality defenses are viable at internet scale. The paper synthesizes historical transition lessons, sector-specific regulatory drivers, and operational constraints in payment infrastructures to derive a new, principle-based migration: crypto-agility, risk-prioritized scoping, hybrid deployment, vendor and supply-chain alignment, independent testing, and proactive supervisory engagement. Acting now reduces long-tail exposure and ensures readiness for imminent compliance and interoperability deadlines.

FinTech doi: 10.3390/fintech5010015

Authors: Md. Shahria Sarker Shuvo Awsaf Tausif Adib Md. Estehaar Ahmed Emon Ahasanur Rafi Rashedur M. Rahman

Stock price forecasting remains challenging due to the non-linear, noisy, and non-stationary nature of financial time series. Although LSTMs and Transformer-based models have improved sequential modeling, their ability to scale efficiently to long financial sequences remains limited. Recently, selective state space models such as Mamba have emerged as efficient alternatives to self-attention, offering attention-like performance with linear computational complexity. In this study, we systematically evaluate multiple Mamba-augmented Transformer architectures for stock market price forecasting. We further propose CrossMamba, a novel architecture that models cross-sequence interactions between encoder and decoder representations using a causal Mamba block. Experiments on multiple S&P 500 and Yahoo Finance stocks show that CrossMamba achieves superior short-horizon performance with 5-day input windows (R2 up to 0.963), while Hybrid Bi-Mamba performs best for longer horizons, achieving the lowest MAE of 0.67 for 10-day forecasts. Compared with advanced Mamba-based and Transformer baselines, the proposed models achieve competitive accuracy while maintaining substantially improved computational efficiency. These results highlight the effectiveness of Mamba-augmented Transformers as scalable architectures for financial time series forecasting.

FinTech doi: 10.3390/fintech5010014

Authors: Helal Uddin Munim Kumar Barai

Asia presently houses some of the top and dynamic economies in the world. These economies have also experienced high fintech adoption in their banking sectors. This paper examines the impact of fintech adoption and integration on the efficiency and stability of banks in 9 Asian countries, using panel data from 85 banks spanning 11 years from 2014 to 2024. It first analyzes the impact of fintech on banks across all selected countries and then, on a stratified basis, divides them into three categories: developed economies, large economies, and emerging countries. The paper uses non-performing loan (NPL) and provision for loan losses (PLLs) as proxies for risk, efficiency ratios, and the cost-to-income ratio as efficiency measures, and the stability ratio and Z-score as indicators of stability. To estimate the results, it has applied ordinary least squares and fixed-effect techniques. The study finds that fintech adoption reduces associated bank risk, presents mixed effects on efficiency, and strongly supports bank stability. Moreover, total assets and ROA consistently demonstrate lower risk, higher efficiency, and greater stability. Overall, the results of this study indicate that fintech encourages greater competition, leading banks to lend more aggressively and, consequently, increasing NPLs, PLLs, and overall risk exposure. Based on the findings, this research suggests that policymakers may adopt fintech strategies to maximize the benefits.

FinTech doi: 10.3390/fintech5010013

Authors: Konstantinos Pantelidis Ioannis Karakostas Odysseas Pavlatos

Extreme movements in financial time series pose challenges for risk management and forecasting, particularly when their timing is irregular and difficult to anticipate. This study aims to develop a probabilistic framework for detecting and predicting such events using daily Bitcoin returns as a case study. We first identify extreme positive and negative return events using the Isolation Forest algorithm and estimate their empirical recurrence patterns using a dynamic frequency table to derive baseline parametric probabilities. A 7-day Hawkes excitation kernel is then applied to capture short-run self-exciting dynamics, and both components are integrated using logistic regression to produce real-time probability forecasts. The results show that positive events occur more frequently than negative ones and that prediction accuracy improves over time: Brier scores, which measure the accuracy of probabilistic predictions, decrease as additional event data accumulate, and log loss values exhibit a consistent downward trend. Overall, by combining anomaly detection, empirical inter-arrival estimation, and excitation dynamics into a unified structure, the proposed framework offers a transparent and adaptable tool for forecasting extreme events in the financial market.

FinTech doi: 10.3390/fintech5010012

Authors: Qinxu Ding Chong Guan Yinghui Yu

We examine whether aggregate “music mood” derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors such as valence, energy, danceability, tempo, loudness, etc.) and Genius-scraped lyrics. We extract lyric sentiment by tokenizing Genius-scraped lyrics and aggregating lexicon-based affect scores (valence and arousal) into popularity-weighted weekly indices. To address sparsity and regime shifts in issuance, we train a leakage-safe Long Short-Term Memory (LSTM) network on a smoothed target—the forward 4-week sum of IPOs—and obtain next-week forecasts by dividing the predicted sum by 4. On a chronological holdout, a single LSTM with look-back K = 8 outperforms strong baselines—reducing MAE by 13.9%, RMSE by 15.9%, and mean Poisson deviance by 27.6% relative to the best baseline in each metric. Furthermore, we adopt SHapley Additive exPlanations (SHAP) to explain our LSTM model, showing that IPO persistence remains the dominant driver, but music and lyrics covariates contribute incremental and robust signal. These results suggest that aggregate music sentiment contains economically meaningful information about near-term IPO activity.

FinTech doi: 10.3390/fintech5010011

Authors: Norshidah Mohamed

Robo-advisors are evolving fintech solutions that ask potential clients about their investment purpose and time horizon and then offer investment strategies to reach different goals. This study aims to build on prior research and gain insights into the influence of innovation attributes (relative advantage, complexity, compatibility, and observability), perceived trust, and image regarding robo-advisor adoption by applying and extending the Diffusion of Innovation (DOI) theory. Data were collected using a cross-sectional survey approach. A total of 187 valid responses were obtained from an online participant recruitment website based in the United States and analysed using the partial least squares approach. The findings indicate that relative advantage and attitude influence an individual’s intention to adopt a robo-advisor, while all innovation attributes, perceived trust, and image of a robo-advisor influence an individual’s attitude towards it. By extending the DOI framework, this research advances understanding of its applicability to robo-advisor adoption. This study contributes to the literature by clarifying the influences on robo-advisor adoption and their relationships. From a practical standpoint, the findings and measures could help wealth management companies improve their promotional campaigns and technical design.

FinTech doi: 10.3390/fintech5010010

Authors: Akila Pramodh Rathnasinghe Ashen Dilruksha Rahubadda Kenneth Arinze Ede Barry Gledson

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails.

FinTech doi: 10.3390/fintech5010009

Authors: Houda Ben Mekhlouf Abdellatif Moussaid Fadoua Ghanimi

Credit card fraud detection remains a critical challenge for financial institutions, particularly due to extreme class imbalance and the continuously evolving nature of fraudulent behavior. This study investigates two complementary approaches: anomaly detection based on multivariate normal distribution and deep reinforcement learning using a Deep Q-Network. While anomaly detection effectively identifies deviations from normal transaction patterns, its static nature limits adaptability in real-time systems. In contrast, the DQN reinforcement learning model continuously learns from every transaction, autonomously adapting to emerging fraud strategies. Experimental results demonstrate that, although initial performance metrics of the DQN are modest compared to anomaly detection, its capacity for online learning and policy refinement enables long-term improvement and operational scalability. This work highlights reinforcement learning as a highly promising paradigm for dynamic, high-volume fraud detection, capable of evolving with the environment and achieving near-optimal detection rates over time.

FinTech doi: 10.3390/fintech5010008

Authors: Salah Kayed Zaid Alhawwatma Amer Morshed Laith T. Khrais

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking.

FinTech doi: 10.3390/fintech5010007

Authors: Claudel Mombeuil Sadrac Jean Pierre

Research on users’ switching intentions in peer-to-peer (P2P) mobile payment systems, particularly in developing markets, remains limited. This study examines how two satisfaction dimensions, transaction-based satisfaction and experience-based satisfaction, influence switching intentions through two layers of trust: institution-based trust and disposition to trust. Grounded in Expectancy-Disconfirmation Theory, data from 529 users of Haiti’s leading P2P mobile payment platform were analyzed using structural equation modeling. Results show that while transaction-based satisfaction has minimal impact on switching intentions, experience-based satisfaction strengthens institution-based trust, which in turn significantly reduces switching intentions. These findings highlight the central role of institutional reliability in shaping post-adoption behavior in duopolistic and resource-constrained markets. The study extends satisfaction-trust theory to digital financial ecosystems and offers practical insights for improving user retention through sustained institutional credibility and long-term service reliability.

FinTech doi: 10.3390/fintech5010006

Authors: Ali Saleh Alshebami

This study investigates the influence of access to mobile cashless technology on enterprise continuity intention and cash flow management skills. It also explores the influence of cashless technology, knowledge, and experience on enterprise continuity intention and cash flow management skills, and examines the direct relationship between cash flow management skills and enterprise continuity intention among micro and small enterprises during crises and in an unstable context. The 259 responses collected from micro and small entrepreneurs were analyzed by Partial Least Squares Structural Equation Modeling. The hypotheses tested reported a positive and significant relationship between access to mobile cashless technology and enterprise continuity intention and cash flow management skills. Furthermore, it was found that cashless technology knowledge and experience have a positive and significant relationship with enterprise continuity intention, as well as cash flow management skills. Finally, cash flow management skills were found to positively influence enterprise continuity intention. The study offers theoretical and practical implications for policymakers and other stakeholders to improve cashless transactions in the context of the study.

FinTech doi: 10.3390/fintech5010005

Authors: Francisco Elieser Giraldo-Gordillo Ricardo Bustillo-Mesanza

This study evaluates the early impact of Central Bank Digital Currencies (CBDCs) on key financial indicators in The Bahamas, focusing on the introduction of the SandDollar—the world’s first fully implemented retail CBDC. Using the Synthetic Control Method (SCM), the analysis constructs counterfactual scenarios to assess the effects of CBDCs on three dependent variables: outstanding loans from commercial banks as a percentage of GDP, outstanding deposits as a percentage of GDP, and the number of deposit accounts per 1000 adults. Three separate SCM models were estimated for the period 2014–2024, incorporating a broad set of control variables reflecting financial infrastructure, economic performance, demographic characteristics, and digital readiness. The findings consistently show that the SandDollar’s implementation is associated with reductions in loan issuance, deposit levels, and deposit account ownership compared to their synthetic counterparts. These results support the hypothesis that direct CBDC models may amplify “deposit substitution” and increase liquidity risks by shifting financial activity away from commercial banks. Although the SCM provides a structured causal framework, the short post-treatment period and potential pandemic-related disruptions limit the scope of a long-term understanding. The study underscores the importance of careful CBDC design, particularly the role of intermediated models in mitigating unintended financial stability risks.

FinTech doi: 10.3390/fintech5010004

Authors: Rasmi Ranjan Khansama Rojalina Priyadarshini Surendra Kumar Nanda Rabindra Kumar Barik

Prediction of stock closing price plays a critical role in financial planning, risk management, and informed investment decision-making. In this study, we propose a novel model that synergistically amalgamates Bidirectional GRU (BiGRU) with three complementary attention techniques—Top-k Sparse, Global, and Bahdanau Attention—to tackle the complex, intricate, and non-linear temporal dependencies in financial time series. The proposed Fused Attention Model is validated on two highly volatile, non-linear, and complex- patterned stock indices: NIFTY 50 and S&P 500, with 80% of the historical price data used for model learning and the remaining 20% for testing. A comprehensive analysis of the results, benchmarked against various baseline and hybrid deep learning architectures across multiple regression performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R2 Score, demonstrates the superiority and noteworthiness of our proposed Fused Attention Model. Most significantly, the proposed model yields the highest prediction accuracy and generalization capability, with R2 scores of 0.9955 on NIFTY 50 and 0.9961 on S&P 500. Additionally, to mitigate the issues of interpretability and transparency of the deep learning model for financial forecasting, we utilized three different Explainable Artificial Intelligence (XAI) techniques, namely Integrated Gradients, SHapley Additive exPlanations (SHAP), and Attention Weight Analysis. The results of these three XAI techniques validated the utilization of three attention techniques along with the BiGRU model. The explainability of the proposed model named as BiGRU based Fused Attention (BiG-FA), in addition to its superior performance, thus offers a robust and interpretable deep learning model for time-series prediction, making it applicable beyond the financial domain.

FinTech doi: 10.3390/fintech5010003

Authors: Mohd Umar Farukh Mohammad Taqi Koteswara Rao Vemavarapu Sayed M. Fadel Nawab Ali Khan

Background: Fintech companies have revolutionized the financial services industry in India in recent years. This is especially true for the growth of digital payment methods. India’s unbanked are being introduced to banking by fintech companies. Despite the country’s strong banking system, many residents find it difficult to get government financial services. This is particularly true for rural or low-income people. This vacuum has been addressed by fintech solutions including digital banking, micro-lending applications, mobile wallets, and UPI platforms. Objectives: to study the impact of financial technology businesses on increasing financial inclusion for India’s underbanked and unbanked population and Challenges encountered by financial technology enterprises in their endeavors to access unbanked populations, encompassing concerns of infrastructure with special reference to western Uttar Pradesh. Method: This mixed-methods study examines how FinTech is narrowing the financial gap for unbanked people using quantitative econometric analysis and qualitative case study assessments. Results: Digital financial innovation and regulatory support encourage inclusive growth in underdeveloped economies, whereas rich nations benefit from sophisticated banking institutions. This is indicated by the small influence of GDP per capita (β = 0.22–0.32, p < 0.05). Findings: The study found that inclusive finance is revolutionized when FinTech is used with the help of robust regulatory frameworks and digital infrastructure. Policymakers should prioritize cybersecurity, public-private partnerships to improve digital literacy, and rural connection if they want more people to take part in the digital financial ecosystem. Implications: FinTech can remove obstacles to accessing financing. The proper coordinated improvements in regulatory frameworks, digital infrastructure and financial literacy among the people are necessary to achieve full financial inclusion.

FinTech doi: 10.3390/fintech5010002

Authors: Volodymyr Evdokimov Anton Kudin Vakhtanh Chikhladze Volodymyr Artemchuk

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline the PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement.

FinTech doi: 10.3390/fintech5010001

Authors: Gulnaz Zakariya Olzhas Akylbekov Aiman Moldagulova Ryskhan Satybaldiyeva

This paper examines the combination of traditional banking credit assessment techniques with contemporary internal sales accounting systems in Kazakhstan, aiming to augment the precision and resilience of financial assessments pertaining to SMEs. The proposed model consists of two discrete components: a traditional credit scoring module that employs logistic regression and a supplementary sales analytics module that leverages ensemble machine learning methodologies — random forests and gradient boosting algorithms. The outputs generated by these components are amalgamated through an ensemble strategy, where optimal weighting coefficients are ascertained via cross-validation. An empirical analysis was conducted on a dataset encompassing 41,000 SME records from a prominent Kazakhstan bank alongside daily transactional sales data from 150 SMEs gathered between the years 2021 and 2024. The integrated hybrid model demonstrated a statistically meaningful enhancement in predictive efficacy, as evidenced by an increase in the area under the ROC curve from 0.76 to 0.87 and a decrease in mean squared error from 0.12 to 0.08 relative to the traditional methodology. The investigation delves into the transformative influence of digitalization on innovation within SMEs, elucidating that improved real-time data integration not only sharpens risk assessment processes but also promotes adaptive lending strategies and operational efficiencies.

FinTech doi: 10.3390/fintech4040077

Authors: Rza Hasanli Mahir Dursun

The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework.

FinTech doi: 10.3390/fintech4040076

Authors: Priscilla Maulina Juliani Siregar Noer Azam Achsani Zenal Asikin Dikky Indrawan

This study develops a comprehensive competitiveness model for Indonesia’s WealthTech ecosystem by integrating Interpretive Structural Modeling (ISM) and MICMAC analysis. The research identifies and classifies 23 interrelated variables derived from SEM-PLS and NVivo analysis, of which 17 passed expert validation and were subsequently retained in the ISM–MICMAC structural model, including innovation capabilities, regulatory support, digital infrastructure, capital readiness, and customer trust, to evaluate their systemic roles in shaping competitive advantage. Through expert interviews, bibliometric analysis, and a structured modeling process, key independent drivers such as innovation capabilities, geopolitical events, and economic shocks were identified as foundational enablers. Linkage variables including digital transformation, strategic alliances, and cost leadership connect these enablers to dependent outcomes such as customer satisfaction and platform personalization. The resulting hierarchical framework and strategic roadmap offer actionable insights for policymakers, fintech stakeholders, and investors to enhance resilience, regulatory alignment, and ecosystem integration. This research not only fills a critical gap in the digital finance literature but also provides a strategic tool for advancing Indonesia’s WealthTech sector within the global financial landscape.

FinTech doi: 10.3390/fintech4040075

Authors: Szilvia Módosné Szalai Szonja Jenei Erzsébet Németh

Purpose: The central research question of the study is how objective financial knowledge and subjective financial confidence interact and relate to digital financial behavior and the use of FinTech tools. By examining both objective knowledge refers to measured, test-based financial competence and subjective confidence denote self-assessed financial understanding, the research offers insight into the psychological and demographic drivers of FinTech use and perceived financial well-being. Design/methodology/approach: Based on the OECD’s 2023 international financial literacy survey, the study uses a nationally representative Hungarian sample. It employs non-parametric statistical methods, linear regression, and two-step cluster analysis. Three composite indicators, general digital activity, digital financial engagement frequency, perceived financial security were developed to measure general digital activity, frequency of digital financial engagement, and perceived financial security. Findings: Results reveal a moderate but significant correlation between actual and self-assessed financial knowledge. Men score higher on both measures, though self-assessment bias does not significantly differ by gender. Higher education and income levels are associated with stronger financial literacy and more frequent use of FinTech tools, while age correlates negatively. However, the accuracy of self-perception is not explained by these demographic factors. Cluster analysis identifies four distinct financial knowledge profiles and five consumer digital behavior types, revealing disparities in digital financial inclusion and confidence. Originality: This research contributes a multidimensional perspective on how consumer capabilities, attitudes, and digital behavior influence FinTech adoption. By integrating behavioral, demographic, and psychological factors, the study offers practical implications for targeted financial education and the design of inclusive, human-centered digital financial services—especially relevant for emerging European markets.

FinTech doi: 10.3390/fintech4040074

Authors: Tan Gürpinar Mehmet Akif Gulum Melanie Martinelli

Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks.

FinTech doi: 10.3390/fintech4040073

Authors: Marta Flamini Maurizio Naldi

Non-Fungible Tokens (NFTs) have transformed digital ownership, offering unique representations of assets such as art, collectibles, and virtual property. However, pricing NFTs remains a complex and underexplored issue. This study addresses two core questions: what determines NFT prices? And how are prices set in NFT markets? We conduct a comprehensive literature review and market analysis to identify both endogenous and exogenous price determinants. Trait rarity emerges as the most influential intrinsic factor, while cryptocurrency value stands out as a major external influence, albeit with ambiguous effects. Other factors include visual aesthetics, scarcity, utility in games, social media engagement, and broader market sentiment. As to pricing mechanisms, aside from fixed pricing (which is accepted in all marketplaces), NFT marketplaces primarily utilise auctions for art pieces and collectibles— especially English and Dutch formats—which are effective at capturing the buyer’s willingness-to-pay.

FinTech doi: 10.3390/fintech4040072

Authors: Regina Veckalne

The rapid growth of financial technology (FinTech) has created new opportunities to promote environmentally responsible consumption. Yet, little is known about the factors that shape consumer trust in green FinTech platforms, which is crucial for their adoption and long-term impact. This study develops and tests a partial least squares structural equation model (PLS-SEM) integrating sustainability and technology determinants of trust. Survey data from 240 consumers were analyzed. Results show that green transparency, platform security and privacy, and ease of use significantly enhance perceived credibility, while social influence and perceived environmental responsibility increase green perceived value. In turn, perceived credibility reduces perceived risk and promotes trust. Trust is also strengthened by environmental responsibility, green perceived value, and platform innovativeness, but weakened by perceived risk. All hypothesized relationships were statistically significant. The findings highlight the importance of credible sustainability communication, high level security, and social endorsement in building trust for green FinTech services.

FinTech doi: 10.3390/fintech4040071

Authors: Fei (Phoebe) Gao Yew Kee Ho Kevin Ow Yong

This study delves into the domain of corporate litigation risk, particularly through the prism of securities class actions, by leveraging financial datasets and trading metrics to broaden the scope of predictability for securities class-action litigation within the wider context of business risks. We delineate the interconnections between class actions and financial risks, further exploring the capability to forecast these risks as precursors to future class actions. We expand financial risk analysis through a nuanced comparative study, utilizing an array of machine learning methodologies. Our findings reveal that decision trees perform well in-sample, though their out-of-sample performance falls short compared to linear models with nonlinear features. However, logistic models, gradient boosting, and k-nearest neighbors (KNN) models show promising results in enhancing out-of-sample performance, albeit at higher computational costs arising from hyperparameter tuning.

FinTech doi: 10.3390/fintech4040070

Authors: Javier Ramos-Díaz Carlos Chengda Xiangyang

This study investigates the critical macro-level conditions that determine the success of crowdfunding platforms, a pivotal segment of the FinTech landscape. We propose a novel configurational theory to decipher how combinations of institutional, economic, and social factors drive platform performance across diverse European economies. Utilizing fuzzy-Set Qualitative Comparative Analysis (fsQCA), we move beyond linear models to reveal that high platform success is not a product of any single factor but emerges from specific, equifinal configurations. Our findings demonstrate that robust crowdfunding ecosystems can thrive even in contexts with less advanced technological infrastructure, provided there is a synergistic interplay of platform governance, institutional trust, regulatory quality, and economic competitiveness. This research contributes to the FinTech literature by reframing crowdfunding success as a complex, context-dependent phenomenon, offering valuable insights for platform developers, regulators, and investors seeking to foster vibrant digital financing environments.

FinTech doi: 10.3390/fintech4040069

Authors: Mukhbira Komilova Marino Nader Richard Ajayi

The contribution of innovation to global development and productivity is indeed very important, as it enables the creation of new products and services or the improvement of existing ones. Experience of developed economies suggests that innovations and Financial Technology (Fintech) evolution have become efficient tools in the process of combating global challenges, such as financial crisis and pandemics. This paper examines the contributions of Fintech in the economic development, proxied by GDP, for Central Asian countries of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan (KKTTU). Indicators of Fintech, regarding these countries, are identified as number of internet subscribers, POS-terminals, number of mobile subscribers, number of people using the internet, and number of people using credit or debit cards. Previous literature provides evidence of the effect of Fintech on the economic fortune of advanced countries. In a similar vein, this paper extends the notion of economic development and Fintech to Central Asia, where it has received little or no attention, except for the case of Kazakhstan. The findings suggest that further research should be directed towards exploring other factors that promote economic and technological collaboration in all countries of the region. Greater success can be derived from synergy of working collaboratively than individually.

FinTech doi: 10.3390/fintech4040068

Authors: Hae Sun Jung Haein Lee

This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.

FinTech doi: 10.3390/fintech4040067

Authors: Claudiu Ioan Negrea Ela Mădălina Scarlat Ionuț Horătău Otilia Manta

The rapid digital transformation of the financial sector has driven supervisory authorities to develop new tools for engaging with fintech innovation. Among these, Innovation Hubs have become essential mechanisms for improving regulatory dialogue, interpretive clarity, and institutional learning. This article examines the Romanian Fintech Innovation Hub (FIH), launched by the National Bank of Romania (NBR) as a consultative platform to support fintech and payment service providers operating within complex legal environments. Using a qualitative, single-case methodology (2019–2023), the study draws on internal NBR documentation, anonymized supervisory materials, and interviews with fintech founders, oversight officers, and policy specialists. The analysis evaluates the Hub’s performance across five key dimensions: stakeholder engagement, regulatory learning, policy calibration, innovation barriers, and institutional reflexivity. Findings reveal that while the Hub strengthened supervisory understanding and enhanced trust-based interaction, its influence on rulemaking and market access was limited by structural and procedural constraints, including resource gaps and the absence of a regulatory sandbox function. Nonetheless, the Romanian experience demonstrates how institutional learning can emerge even in bank-dominated markets, generating internal adaptation and improving fintech compliance readiness. Comparative insights from Hungary and Italy highlight the advantages of modular, risk-proportionate engagement models that integrate advisory and testing functions. The study contributes to the theory of adaptive regulation by proposing that innovation hubs function as feedback loop mechanisms linking market experimentation with supervisory evolution, offering a replicable model for small and emerging financial systems seeking to balance innovation facilitation with prudential soundness and legal certainty. As such, it provides generalizable insights for central banks and government policymakers on developing FinTech hubs that balance innovation facilitation with prudential soundness and legal certainty.

FinTech doi: 10.3390/fintech4040066

Authors: Hamid Nach

As Morocco aspires to position itself as a regional hub for financial innovation in Africa, its Fintech sector presents a paradox: despite a robust digital infrastructure and growing institutional support, adoption remains limited. Systemic barriers—such as a persistent cash-based culture, low mobile money usage, and fragmented collaboration—continue to impede the sector’s growth. Against this backdrop, this study applies the Delphi research method to systematically identify and prioritize the most pressing challenges and strategic actions facing Morocco’s Fintech ecosystem. Drawing on the insights of 45 experts from finance, technology, academia, startups, and service-oriented organizations, the study follows a three-phase process: open-ended brainstorming, narrowing down, and final ranking. The process produced consensus around 12 key challenges and 12 strategic actions, including the need for an open banking framework, a unified national Fintech vision, regulatory sandboxes, and improved collaboration between incumbents and startups. These findings offer actionable insights to Moroccan policymakers and industry leaders and contribute to Fintech research in emerging economies.

FinTech doi: 10.3390/fintech4040065

Authors: Miguel Resende

Advances in Artificial Intelligence (AI) and Large Language Models (LLMs) have transformed accounting by automating repetitive tasks and enhancing the efficiency of financial reporting. However, their implementation raises challenges related to bias, reliability, and professional adaptation. This article evaluates the comparative performance of three approaches to the vertical analysis of income statements: the traditional manual process, a specialized GPT model, and an AI agent integrating GPT with no-code automation tools. Using the Design Science Research (DSR) methodology, 150 experimental analyses were conducted to measure the execution time, variability, and process scalability. The results indicate that GPT substantially reduced execution time compared to the manual baseline, but still required significant human intervention. The AI agent achieved the greatest gains, reducing the average execution time by nearly 75%, while also demonstrating more stable performance and minimizing the repetitive workload. These findings provide empirical evidence that agent-based automation enhances both efficiency and reliability in accounting workflows, reinforcing its potential to reshape professional practice by reallocating human effort to validation and analytical tasks.

FinTech doi: 10.3390/fintech4040064

Authors: Laith Almaqableh Maher Khasawneh Mehmet Sahiner

The environmental footprint of cryptocurrency networks, particularly the electricity-intensive Bitcoin (BTC) blockchain, has raised growing concern among policymakers, investors, and environmental organizations. This study examines how major global environmental events and climate policy announcements influence Bitcoin’s return and risk dynamics, linking digital asset markets to sustainability debates. Thirteen events between 2010 and 2024—including multilateral agreements (e.g., the Paris Agreement), COP summits, extreme weather disasters, and national policy interventions—are analyzed using an event study framework integrated with the Capital Asset Pricing Model (CAPM) and GARCH-based volatility modelling. We hypothesize that highly visible policy events generate stronger short-run abnormal returns than climate disasters, while disasters produce more persistent effects on volatility. Results confirm this distinction: events such as the U.S. Paris Agreement withdrawal triggered immediate and significant reactions, whereas major weather disasters induced longer-term volatility adjustments. While overall systematic risk remained stable, event-specific responses revealed shifts in Bitcoin’s sensitivity to global equity markets. Climate-related signals shape speculative digital asset markets, with implications for sustainable finance, climate risk assessment, and regulatory policy design. Climate-related news can shape investor perceptions of energy-intensive digital assets, with implications for environmental policy design, sustainable finance strategies, and climate risk assessment. For policymakers, the results highlight the potential of environmental signals to influence speculative markets, supporting the case for integrating financial market behaviour into environmental management and regulatory planning.

FinTech doi: 10.3390/fintech4040063

Authors: Pallavi Ranjan Rania Itani Alessio Faccia

Deep learning has transformed numerous areas of data science by achieving outstanding performance in tasks such as image recognition, speech processing, and natural language understanding. Recently, the challenges of financial forecasting—marked by nonlinear dynamics, volatility, and regime shifts—have attracted increasing attention from the deep learning community. Among these approaches, Convolutional Neural Networks (CNNs), originally developed for spatial data, have shown strong potential for modelling financial time series. This study presents an interpretable CNN-based framework for stock price forecasting using the S&P 500 index as a case study. The proposed approach integrates historical price data with technical indicators within a unified experimental design and compares performance against traditional statistical (ARIMA) and sequential deep learning (LSTM) baselines. Empirical results demonstrate that the CNN model achieves superior predictive Accuracy while maintaining computational efficiency and interpretability through SHAP and Grad-CAM analyses. The findings suggest that lightweight CNN architectures can serve as effective, transparent tools for short-horizon financial forecasting, and future research may extend this framework to multimodal settings incorporating sentiment or news-based data.

FinTech doi: 10.3390/fintech4040062

Authors: Ari Warokka Dewi Sartika Aina Zatil Aqmar

FinTech-based lending has rapidly expanded in emerging economies, offering convenience and inclusion but also raising concerns about over-indebtedness. In Indonesia, the surge of digital loans has been accompanied by growing signs of risky borrowing behavior, including late payments, high debt-to-income ratios, and poor credit discipline. This study investigates the determinants of individuals’ propensity to indebtedness in FinTech-based loans, focusing on the influence of financial behavior biases, emotions, culture, and materialism, as well as the moderating effects of financial literacy, job security, and religiosity. Data were collected from 400 Indonesian civil servants and private/self-employed workers through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that all proposed determinants significantly increase indebtedness, with financial behavior biases having the strongest impact. Financial literacy and job security amplify these effects, while religiosity weakens the influence of emotions and materialism. These findings contribute to behavioral finance theory and underscore the importance of promoting financial literacy, strengthening job stability, and integrating responsible lending policies to mitigate debt risks in emerging economies.

FinTech doi: 10.3390/fintech4040061

Authors: Minh Vo

This study provides a comprehensive evaluation of five deep learning (DL) architectures—TiDE, LSTM, DeepAR, TCN, and Transformer—against the extended Heterogeneous Autoregressive (HAR) model for stock market volatility forecasting. Utilizing 22.5 years of high-frequency data from the S&P 500, DJIA, and Nasdaq indices and incorporating key macroeconomic variables (DXY, VIX, US10Y, and US1M), we assess predictive accuracy across multiple horizons from one day to one month. Our analysis yields three main findings. First, when macroeconomic variables are included, DL models consistently and significantly outperform the HAR benchmark, with TiDE excelling in one-day-ahead predictions and DeepAR dominating longer horizons. Second, in the absence of these exogenous variables, the statistical advantage of DL models over HAR often disappears, highlighting HAR’s enduring relevance in feature-constrained settings. Third, among the DL architectures, DeepAR emerges as the most robust and versatile performer, especially when leveraging macroeconomic data. These results underscore the conditional power of deep learning and provide practical guidance on model selection for financial practitioners and researchers.

FinTech doi: 10.3390/fintech4040060

Authors: Vesna Buterin Dragan Čišić Ivan Gržeta

Economic disinformation causes significant harm, resulting in substantial losses for the global economy. Each year, it is estimated that around USD 78 billion is lost due to the spread of false or misleading information, with a major share stemming from stock market fluctuations and misguided decisions. In Croatia, the rapid spread of economic misinformation further threatens decision-making and institutional credibility. The EkonInfoChecker project was established to address this issue by combining human fact-checking with AI-based detection. This paper presents the project’s AI component, which adapts English-language datasets (FakeNews Corpus 1.0 and WELFake) into Croatian, yielding over 170,000 articles in economics, finance, and business. We trained and evaluated six models—FastText, NBSVM, BiGRU, BERT, DistilBERT, and the Croatian-specific BERTić—using precision, recall, F1-score, and ROC-AUC. Results show that transformer-based models consistently outperform traditional approaches, with BERTić achieving the highest accuracy, reflecting its advantage as a language-specific model. The study demonstrates that AI can effectively support fact-checking by pre-screening economic content and flagging high-risk items for human review. However, limitations include reliance on translated datasets, reduced performance on complex categories such as satire and pseudoscience, and challenges in generalizing to real-time Croatian media. These findings underscore the need for native datasets, hybrid human-AI workflows, and governance aligned with the EU AI Act.

FinTech doi: 10.3390/fintech4040059

Authors: Abdelhalem Mahmoud Shahen Mesbah Fathy Sharaf

In this study, we review recent research on how digital payment technologies (DPTs) promote financial inclusion (FI) across the world. Drawing on empirical studies from the past decade, we show that digital payment systems have helped reduce financial exclusion—particularly in developing economies—by expanding access to essential financial services for underserved groups. The paper also highlights the role of demographic factors such as age and gender, with evidence of higher adoption among youth and women. We identify the main indicators used to measure digital payment adoption and FI, providing a foundation for future empirical analysis. To deepen understanding, we call for combining macroeconomic data with rigorous econometric approaches to better capture how DPTs contribute to inclusive financial systems. The paper further discusses how emerging innovations—including blockchain, artificial intelligence, cloud computing, and biometric authentication—are improving the efficiency, security, and accessibility of digital payments. Together, these technologies are likely to accelerate the transition toward fully digital financial ecosystems and expand the potential for inclusive and sustainable growth.

FinTech doi: 10.3390/fintech4040058

Authors: Motomi Yoshioka Yoshiaki Nose Yoshihiro Mori

In this study, we empirically examine the determinants of fundraising success in Japan’s lending-based crowdfunding (LBCF), with a focus on the financial literacy of investors. Using 465 campaigns on the LBCF platform “Bankers” (December 2020–September 2024), we test two predictions derived from the lack of financial literacy hypothesis: (H1) investors are influenced by peripheral signals; (H2) diagnostic signals are not properly evaluated. Both are rejected. In cross-sectional tests, peripheral cues such as “Perks” are negatively associated with success, and the effects of “Title length” and “Purple highlighted text” observed in simpler models vanish when analyzed jointly. By contrast, diagnostic information is consistently informative: “Domestic campaign” and “Co-investment” are positive, while “Investment term” is negative; “Investment capital” is also negative, contrary to prior expectations. The results are robust to controls for the campaign sector and to alternative specifications (probit; OLS on achievement rate). Overall, investors in Japan’s LBCF appear to rely on diagnostic rather than peripheral signals, indicating financially literate, rational decision-making.

FinTech doi: 10.3390/fintech4040057

Authors: Ihsen Abid

This study investigates the dynamic relationship between key macroeconomic indicators, specifically inflation (CPI), the Federal Funds Rate, GDP growth, unemployment, and money supply, and U.S. stock market returns, represented by the S&P 500 index, over the period January 2015 to June 2025. The objective is to understand how inflation and monetary policy affect market performance in both the short and long run. Using an Autoregressive Distributed Lag (ARDL) modeling framework and Error Correction Model (ECM), the study examines monthly S&P 500 returns alongside macroeconomic variables, accounting for lagged effects and potential cointegration. The model captures both immediate and delayed impacts, employing the Bounds Testing approach to confirm long-run equilibrium relationships. Results show significant mean-reversion in stock returns, a delayed negative impact of inflation and interest rate increases, and a positive contemporaneous response to GDP growth. Unemployment exhibits a counterintuitive positive effect on returns, suggesting forward-looking investor expectations. The money supply also positively influences equity prices, supporting liquidity-based asset pricing theories. This paper provides updated empirical evidence on macro-finance linkages and highlights the complex interplay of monetary policy, inflation, and market expectations in shaping U.S. equity returns.

FinTech doi: 10.3390/fintech4040056

Authors: Deepak Kumar Priyanka Pramod Pawar Santosh Reddy Addula Mohan Kumar Meesala Oludotun Oni Qasim Naveed Cheema Anwar Ul Haq

This study proposes a novel approach to improve the reliability of trading signals for gold market prediction by integrating technical analysis indicators, Moving Averages (MAs), MACD, and Ichimoku Cloud, with a Particle Swarm-Optimized Extreme Learning Machine (PSO-ELM). Traditional time-series models often fail to capture the complex, non-linear dynamics of financial markets, whereas technical indicators combined with machine learning enhance predictive accuracy. Using daily gold prices from January–October 2020, the PSO-ELM model demonstrated superior performance in filtering false signals, achieving high precision, recall, and overall accuracy. The results highlight the effectiveness of combining technical analysis with machine learning for robust signal validation, providing a practical framework for traders and investors. While focused on gold, this methodology can be extended to other financial assets and market conditions. The integration of machine learning and blockchain enhances both predictive reliability and operational trust, offering traders, investors, and institutions a robust framework for decision support in dynamic financial environments.

FinTech doi: 10.3390/fintech4040055

Authors: John Robert Hamilton Dil Nawaz Hakro

iFintech is termed as an Islamic alternative banking and financial services approach to that of existing, Fintech digital, Western democracies banking and financial systems. This Pakistan Islamic digital banking and financial services technologies (iFintech) study engages a qualitative NVivo study, and a quantitative covariance based structural equation modelling (CB-SEM) study to assess how young, tech savvy, capital city respondents likely approach their readiness to adopt iFintech. Study data engages qualitative assessments and quantitative framework modelling. Research findings show a competencies and capabilities framework enlists three major pathways (economic worth, social acceptance, plus technical transfer associated risks) that can influence iFintech adoption readiness. This empirical study presents a new, robust, iFintech adoption readiness approach which predominantly Islamic countries like Pakistan may choose to consider when encouraging their young, tech savvy, capital city residents towards adopting digital banking and financial services within their nation.

FinTech doi: 10.3390/fintech4040054

Authors: Alexandros Koulis Constantinos Kyriakopoulos Ioannis Lakkas

Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes.

FinTech doi: 10.3390/fintech4040053

Authors: Anurag Dutta M. Gayathri Lakshmi A. Ramamoorthy Pijush Kanti Kumar

Large language models have expanded their application from traditional tasks in natural language processing to several domains of science, technology, engineering, and mathematics. This research studies the potential of these models for financial “nowcasting”–real-time forecasting (of the recent past) for cryptocurrency market conditions. Further, the research benchmarks capabilities of five state-of-the-art decoder-only models, gpt-4.1 (OpenAI), gemini-2.5-pro (Google), claude-3-opus-20240229 (Anthropic), deepseek-reasoner (DeepSeek), and grok-4 (xAI) across 12 major crypto-assets around the world. Using minute-resolution history of a day in USD for the stocks, gemini-2.5-pro emerges as a consistent leader in forecasting (except for a few assets). The stablecoins exhibit minimal deviation across all models, justifying the “nowcast strength” in low-volatility environments, although they are not able to perform well for the highly erratic assets. Additionally, since large language models have been known to better their performance when executed for a higher number of passes, the experimentations were conducted for two passes (Pass@1 and Pass@2), and the respective nowcast errors are found to be reduced by 1.2156% (on average).

FinTech doi: 10.3390/fintech4040052

Authors: Huma Habib Shadan Sardar M. N. Islam

Quantum technology is significantly transforming businesses, organisations, and information systems. It will have a significant impact on accounting and finance, particularly in the context of cybersecurity. It presents both opportunities and risks in maintaining confidentiality and protecting financial data. This study aims to demonstrate the application of quantum technologies in accounting cybersecurity, utilising quantum algorithms and QKD to overcome the limitations of classical computing. The literature review emphasises the vulnerabilities of current accounting cybersecurity to quantum attacks and highlights the necessity for quantum-resistant cryptographic mechanisms. It discusses the risks related to traditional encryption methods within the context of quantum capabilities. This research enhances understanding of how quantum computing can revolutionise accounting cybersecurity by advancing quantum-resistant algorithms and implementing QKD in accounting systems. This study employs the PSALSAR systematic review methodology to ensure thoroughness and rigour. The analysis shows that quantum computing pushes encryption techniques beyond classical limits. Using quantum technologies in accounting reduces data breaches and unauthorised access. This study concludes that quantum-resistant algorithms and quantum key distribution (QKD) are crucial for securing the future of accounting and finance systems.

FinTech doi: 10.3390/fintech4030051

Authors: Arthur Rodrigues Pereira de Carvalho Felipe Quintino Helton Saulo Luan C. S. M. Ozelim Tiago A. da Fonseca Pushpa N. Rathie

This study introduces and evaluates stochastic models to describe Bitcoin price dynamics at different time scales, using daily data from January 2019 to December 2024 and intraday data from 20 January 2025. In the daily analysis, models based on are introduced to capture long memory, paired with both constant-volatility (CONST) and stochastic-volatility specifications via the Cox–Ingersoll–Ross (CIR) process. The novel family of models is based on Generalized Ornstein–Uhlenbeck processes with a fluctuating exponential trend (GOU-FE), which are modified to account for multiplicative fBm noise. Traditional Geometric Brownian Motion processes (GFBM) with either constant or stochastic volatilities are employed as benchmarks for comparative analysis, bringing the total number of evaluated models to four: GFBM-CONST, GFBM-CIR, GOUFE-CONST, and GOUFE-CIR models. Estimation by numerical optimization and evaluation through error metrics, information criteria (AIC, BIC, and EDC), and 95% Expected Shortfall (ES95) indicated better fit for the stochastic-volatility models (GOUFE-CIR and GFBM-CIR) and the lowest tail-risk for GOUFE-CIR, although residual analysis revealed heteroscedasticity and non-normality. For intraday data, Exponential, Weibull, and Generalized Gamma Autoregressive Conditional Duration (ACD) models, with adjustments for intraday patterns, were applied to model the time between transactions. Results showed that the ACD models effectively capture duration clustering, with the Generalized Gamma version exhibiting superior fit according to the Cox–Snell residual-based analysis and other metrics (AIC, BIC, and mean-squared error). Overall, this work advances the modeling of Bitcoin prices by rigorously applying and comparing stochastic frameworks across temporal scales, highlighting the critical roles of long memory, stochastic volatility, and intraday dynamics in understanding the behavior of this digital asset.

FinTech doi: 10.3390/fintech4030050

Authors: Nikiforos T. Laopodis Eleftheria Kostika

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 [...]

FinTech doi: 10.3390/fintech4030049

Authors: Amna Albuainain Simon Ashby

The rise of financial technology (FinTech) has generated substantial research on its adoption by customers and the associated implications for traditional banks. This systematic review addresses two questions: (1) What factors enable or hinder consumer adoption of FinTech? (2) How does consumer adoption of FinTech affect the performance of traditional banks? Following the PRISMA guidelines, we screened and analyzed 109 peer-reviewed articles published between 2016 and 2024 in Scopus and Web of Science. The findings show that adoption is driven by economic incentives, digital infrastructure, personalized services, and institutional support, while barriers include limited literacy, perceived risk, and regulatory uncertainty. At the bank level, adoption enhances operational efficiency, customer loyalty, and revenue growth but also generates compliance costs, cybersecurity risks, and competition. Consumer adoption studies primarily employ the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), often extended with trust and privacy constructs. In contrast, bank performance research relies on empirical analyses with limited theoretical grounding. This review bridges behavioral and institutional perspectives by linking consumer-level drivers of adoption with organizational outcomes, offering an integrated conceptual framework. The limitations include a restriction of the retrieved literature to English publications in two databases. Future work should apply longitudinal, multi-theory models to deepen the understanding of how consumer behavior shapes bank performance.

FinTech doi: 10.3390/fintech4030048

Authors: William Gaviyau Jethro Godi

Banking has evolved from ancient times of using grain banks and temple lending to modern banking practices. The transformation of the banking sector has ensured that banks play the crucial role of facilitating faster and efficient service delivery. This paper traced the evolution of banking and examined associated disruptions, opportunities, and challenges. With the specific objective of influencing policy-oriented discussions on the future of banking, this study adopted a literature review methodology of integrating various sources, such as scholarly journals, policy reports, and institutional publications. Public interest theory and disruptive innovation theory underpinned this study. Findings revealed that banking has evolved from Banking 1.0 to Banking 5.0 due to disruptive factors which have been pivotal to the significant structural sector changes: Banking 1.0 (pre-1960s); Banking 2.0 (1960s to 1980s); Banking 3.0 (1980s–2000s); Banking 4.0 (2000s–2020s); and Banking 5.0 (2020s to the future). Despite the existence of opportunities in the transformation, challenges include regulations, skills shortages, legacy systems, and cybersecurity that must be addressed. This calls for a coordinated response from stakeholders, with banking’s future requiring collaborations as cashless economies, digital economies, and digital currencies take centre stage.

FinTech doi: 10.3390/fintech4030047

Authors: Dimitrios Varveris Vasiliki Basdekidou Chrysanthi Basdekidou Panteleimon Xofis

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future.

FinTech doi: 10.3390/fintech4030046

Authors: Ivanka Vasenska

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R2 scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols.

FinTech doi: 10.3390/fintech4030045

Authors: Nour Mohamad Fayad Ali Awdeh Jessica Abou Mrad Ghaithaa El Mokdad Madonna Nassar

This study investigates the impact of digitalisation and intangible investment—specifically digital skills and software adoption—on productivity in the United Kingdom’s banking sector. Software adoption is captured through banks’ investment in enterprise systems (CRM/ERP, cloud computing, and related applications), rather than a single software version. Drawing on detailed bank-level data from six major UK banks over the period 2007–2022, this research provides empirical evidence that higher intensities of digital human capital and intangible assets are positively associated with improvements in both employee productivity and overall bank performance. A standard deviation increase in software specialist employment is associated with productivity gains of 10.3% annually, though this upper-bound estimate likely combines direct effects with complementary factors such as concurrent IT investments (e.g., cloud infrastructure) and managerial innovations. The findings also highlight substantial heterogeneity across banks, with younger institutions experiencing more pronounced benefits from intangible investment due to their greater flexibility and innovation capacity. Furthermore, this study reveals that the adoption of high-speed internet and investment in IT hardware have a strong positive effect on bank productivity, particularly in the wake of the COVID-19 pandemic, which accelerated digital transformation across the sector. However, the observational nature of the study and the limited sample size necessitate caution in generalising the findings. While the results have implications for digital workforce development and technology infrastructure, policy recommendations should be interpreted as preliminary, pending further validation in broader samples and diverse institutional settings. This study concludes by advocating for targeted strategies to expand digital skills, promote software diffusion, and modernise infrastructure to facilitate productivity convergence, while emphasising the need for future research to address potential endogeneity and external validity limitations.

FinTech doi: 10.3390/fintech4030044

Authors: Greta Marcevičiūtė Kamilė Taujanskaitė Jens Kai Perret

The new era of FinTech services enabled the financial sector to benefit from innovative and cost-effective products via process automation, fostering a foundation for more sustainable business growth. Despite considerable research, the determinants of FinTech services adoption by businesses remain mostly unknown. For the first time, a mixed-method study is realized combining the perspectives of FinTech services providers (experts) and FinTech service users (businesses that use FinTech). To elicit the providers’ views, interviews have been conducted with experts from FinTech service providers. From the user side, data generated via online surveys was evaluated in an adjusted Unified Theory of Acceptance and Use of Technology (UTAUT2) model tailored to FinTech specifics using the R implementation of PLS-SEM. The results of this analysis enabled comparisons between the perspectives of providers and users to identify similarities and differences in adoption factors. Correspondingly, conclusions on FinTech adoption encourage FinTech service providers to adjust their solutions to better fit the business requirements. For business owners, they provide valuable insights on how to streamline their financials and foster sustainable growth through efficiency gains.

FinTech doi: 10.3390/fintech4030043

Authors: Alessia Rufolo Tetiana Paientko Katrin Dziergwa

This study examines the impact of mergers and acquisitions (M&As) on the financial performance of firms listed in Germany’s DAX 40 index. Although M&As are a widely used strategic tool intended to create value through synergies and market expansion, existing research provides conflicting evidence about their effectiveness. Using an empirical approach, we analyze the financial data of acquiring companies before and post-M&A transactions to evaluate changes in profitability, liquidity and solvency. Our findings suggest that financial performance does not universally improve following acquisitions. Instead, results vary significantly based on deal characteristics and internal management factors. These results suggest that, while M&A can be a pathway to growth, success depends heavily on the quality of execution and organizational integration. This paper contributes to the ongoing debate about the effectiveness of M&As and provides insights for corporate decision-makers, investors, and policy stakeholders.

FinTech doi: 10.3390/fintech4030042

Authors: Zilong Liu Hongyan Liang

This study examines whether fintech lenders outperform traditional banks and non-fintech non-banks in risk assessment for U.S. 30-year fixed-rate conforming mortgages. Analyzing Fannie Mae and Freddie Mac loans from Q1 2012 to Q1 2020 using ROC/AUC and risk-pricing regressions, we find fintech lenders have lower predictive accuracy and pricing misalignment, charging higher rates to borrowers who remain current and lower rates to those who default or prepay. These results indicate that conforming mortgage regulations and rapid loan sales to government-sponsored enterprises (GSEs) diminish fintech firms’ incentives for enhanced borrower screening, thus reducing their risk assessment effectiveness.

FinTech doi: 10.3390/fintech4030041

Authors: Xue Yuan Chin-Hong Puah Dayang Affizzah binti Awang Marikan

The comprehensive integration of modern technologies, such as artificial intelligence and big data, into the financial sector in recent years has profoundly transformed the operating model of the traditional financial industry. These technologies not only redefine the operating mechanisms of the financial industry but also significantly reshape the competitive landscape and strategic development of commercial banks. To investigate the impact of FinTech on the overall profitability of commercial banks, this study utilizes a balanced panel dataset comprising 50 listed commercial banks in China from 2012 to 2023 and conducts an empirical analysis based on a fixed-effects model. The findings reveal that, from a dynamic perspective, there exists a significant U-shaped relationship between FinTech and the comprehensive profitability of commercial banks, with a development threshold of 2.86. When the level of FinTech development falls below this critical threshold, its impact on the profitability of commercial banks is predominantly negative. However, once FinTech development surpasses this threshold, its positive effects on enhancing the profitability of commercial banks gradually emerge. Therefore, the government should provide phased policy support to achieve both short-term burden reduction and long-term innovation, and commercial banks should adopt FinTech development as a long-term strategic priority.

FinTech doi: 10.3390/fintech4030040

Authors: Iftekhar Ahmed Robin Mohammad Mazharul Islam Majed Alharthi

This paper examines the impact of agent banking activities, a recent FinTech development, influencing the profitability and financial outcomes of commercial banks operating in Bangladesh, as agent banking has been receiving significant global attention due to its technology-driven approach, cost-effectiveness and easy accessibility, and broader coverage of the unbanked population. Through the application of penal data regression methods, the study estimates a random-effect model using panel data comprising quarterly observations from nine Bangladeshi commercial banks that maintained uninterrupted agent banking activities, covering both deposit mobilization and lending during the period from 2018Q1 to 2024Q4. The empirical findings indicate that credit disbursement by agent banks has a positive and statistically significant impact on bank profitability measures, return on assets (ROA), and return on equity (ROE). Similarly, the expansion of agent banking outlets positively and significantly influences ROA. Therefore, an appropriate agent banking policy aimed at increasing agent banking outlets using digital platforms based on FinTech is vital for ensuring positive growth in credit disbursement to achieve improved financial outcomes for the banking sector in a developing country like Bangladesh.

FinTech doi: 10.3390/fintech4030039

Authors: Francisco Elieser Giraldo-Gordillo Ricardo Bustillo-Mesanza

This paper measures the post-treatment effects of Central Bank Digital Currencies (CBDCs) on mobile money and outstanding loans from commercial banks as a percentage of the GDP in Nigeria and the Bahamas, respectively, from the perspective of financial inclusion. The literature on the topic has primarily focused on the technological specifications of CBDCs and their potential future implementation. This article addresses a gap in the empirical literature by examining the effects of CBDCs. To this end, a Synthetic Control Method (SCM) is applied to the Bahamas (SandDollar) and Nigeria (eNaira) to construct a counterfactual scenario and assess the impact of CBDCs on mobile money and commercial bank loans. Nigeria’s mobile money transactions as a percentage of the GDP increased significantly compared to the synthetic control group, suggesting a notable positive effect of the eNaira. Conversely, in the Bahamas, actual performance fell below the synthetic control, implying that SandDollar may have contributed to a decline in outstanding loans. These results suggest that CBDCs could pose a “deposit substitution risk” for commercial banks. However, they may also enhance the performance of other Fintech tools, as observed in the case of mobile money. As CBDC implementations worldwide remain in their early stages, their long-term effects require further analysis.

FinTech doi: 10.3390/fintech4030038

Authors: Konstantinos S. Skandalis Dimitra Skandali

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies.

FinTech doi: 10.3390/fintech4030037

Authors: Vasiliki Basdekidou Harry Papapanagos

This paper explores the intervening role in SEP performance of corporate environmental, cultural, and ethnic activities (ECEAs) and diversity, equity, inclusion, and social initiatives (DEISIs) on blockchain adoption (BCA) strategy, particularly useful in the Western Balkans (WB), which demands transparency due to extended fraud and ethnic complexities. In this domain, a question has been raised: In BCA strategies, is there any correlation between SEP performance and ECEAs and DEISIs in a mediating role? A serial mediation model was tested on a dataset of 630 WB and EU companies, and the research conceptual model was validated by CFA (Confirmation Factor Analysis), and the SEM (Structural Equation Model) fit was assessed. We found a statistically sound (significant, positive) correlation between BCA and ESG success performance, especially in the innovation and integrity ESG performance success indicators, when DEISIs mediate. The findings confirmed the influence of technology, and environmental, cultural, ethnic, and social factors on BCA strategy. The findings revealed some important issues of BCA that are of worth to WB companies’ managers to address BCA for better performance. This study adds to the literature on corporate blockchain transformation, especially for organizations seeking investment opportunities in new international markets to diversify their assets and skill pool. Furthermore, it contributes to a deeper understanding of how DEI initiatives impact the correlation between business transformation and socioeconomic performance, which is referred to as the “social impact”.

FinTech doi: 10.3390/fintech4030036

Authors: Jihyung Han Daekyun Ko

The rapid growth of mobile financial services provides significant opportunities for enhancing digital financial inclusion among older adults. However, elderly consumers often lag in adoption and sustained usage due to psychological barriers (e.g., technology anxiety) and factors related to prior experience and comfort with technology (e.g., technology familiarity). This study investigates how technology anxiety and technology familiarity influence elderly consumers’ continuance intention toward mobile banking, while examining age as a moderator by comparing younger older adults (aged 60–69) and older adults (aged 70+). Using data from an online survey of 488 elderly mobile banking users in South Korea, we conducted hierarchical regression analyses. The results show that technology anxiety negatively affects continuance intention, whereas technology familiarity positively enhances sustained usage. Moreover, age significantly moderated these relationships: adults aged 70+ were notably more sensitive to both technology anxiety and familiarity, highlighting distinct age-related psychological differences. These findings underscore the importance of targeted digital literacy initiatives, age-friendly fintech interfaces, and personalized support strategies. This study contributes to the fintech literature by integrating psychological dimensions into traditional technology adoption frameworks and emphasizing age-specific differences. Practically, fintech providers and policymakers should adopt tailored strategies to effectively address elderly consumers’ unique psychological needs, promoting sustained adoption and narrowing the digital divide in financial technology engagement.

FinTech doi: 10.3390/fintech4030035

Authors: Nesrine Gafsi

This paper considers the impact of Central Bank Digital Currencies (CBDCs) on the world’s financial systems with a special emphasis on G20 economies. Using quarterly macro-financial data for the period of 2000 to 2024, collected from the IMF, BIS, World Bank, and Atlantic Council, a Global Vector Autoregression (GVAR) model is applied to 20 G20 countries. The results reveal significant heterogeneity across economies: CBDC shocks intensify emerging market financial instability (e.g., India, Brazil), while more digitally advanced countries (e.g., UK, Japan) experience stabilization. Retail CBDCs increase disintermediation risks in more fragile banking systems, while wholesale CBDCs improve cross-border liquidity. This article contributes to the literature by providing the first GVAR-based estimation of CBDC spillovers globally.

FinTech doi: 10.3390/fintech4030034

Authors: Haris Alibašić

The integration of artificial intelligence and human decision-making within blockchain systems has raised complex ethical considerations, necessitating the development of comprehensive theoretical frameworks. This research develops a multi-paradigm ethical framework addressing the ethical dimensions of hybrid intelligence—the dynamic interplay between human judgment and artificial intelligence—in the governance of blockchain technology and cryptocurrency systems. Drawing upon complexity theory and institutional theory, this study employs a theory synthesis methodology to investigate inherent paradoxes within hybrid intelligence systems, including how transparency creates new opacities in AI decision-making, decentralization enables centralized control, and algorithmic efficiency undermines ethical sensitivity. Through PRISMA-compliant systematic literature analysis of 50 relevant publications and theoretical synthesis, this research demonstrates how blockchain technology fundamentally redefines hybrid intelligence by establishing novel forms of trust, accountability, and collective decision-making. The framework advances three testable propositions regarding emergent intelligence properties, adaptive capacity, and institutional legitimacy while providing practical governance principles and implementation methodologies for blockchain developers, regulators, and participants. This study contributes theoretically by bridging the fields of complex systems and institutional analysis, integrating complex adaptive systems with institutional legitimacy processes through a multi-paradigm integration methodology. It delivers an ethical framework that addresses accountability distribution in Decentralized Autonomous Organizations, quantifies ethical challenges across major platforms, and offers empirically validated guidelines for balancing algorithmic autonomy with human oversight in decentralized systems.

FinTech doi: 10.3390/fintech4030033

Authors: Mohammed Ibrahim Hussain Arslan Munir Mohammad Mamun Safiul Haque Chowdhury Nazim Uddin Muhammad Minoar Hossain

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) models, namely Extreme Gradient Boosting Regression (XGBR), random forest regression (RFR), Categorical Boosting Regression (CBR), Adaptive Boosting Regression (ADBR), and Gradient Boosted Decision Trees Regression (GBDTR), on a comprehensive dataset. We used a dataset with 1000 samples with eight features and a secondary dataset for external validation with 3865 samples. Our integrated approach identifies Categorical Boosting with GA (CBRGA) as the best performer, achieving an R2 of 0.9973 and outperforming existing state-of-the-art methods. ANOVA-based analysis further enhances model interpretability and performance by isolating key factors such as square footage and lot size. To ensure robustness and transparency, we conduct 10-fold cross-validation and employ explainable AI techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing insights into model decision-making and feature importance.

FinTech doi: 10.3390/fintech4030032

Authors: Fernando Henrique Taques

The digital economy has amplified the role of technological innovation in transforming financial services and business models. Patent data offer valuable insights into these dynamics, especially within the growing FinTech ecosystem. This study conducts a bibliometric analysis of academic research that utilizes PATSTAT, a global database managed by the European Patent Office, focusing on its application in studies related to digital innovation, finance, and economic transformation. A systematic mapping of publications indexed in Scopus, Web of Science, Wiley, Emerald, and Springer Nature is carried out using Biblioshiny and Bibliometrix in RStudio 2025.05.0, complemented by graph-based visualizations via VOSviewer 1.6.20. The findings reveal a growing body of research that leverages PATSTAT to explore technological trajectories, intellectual property strategies, and innovation systems, particularly in areas such as blockchain technologies, AI-driven finance, digital payments, and smart contracts. This study contributes to the literature by highlighting the strategic value of patent analytics in the FinTech landscape and offers a reference point for researchers and decision-makers aiming to understand emerging trends in financial technologies and the digital economy.

FinTech doi: 10.3390/fintech4030031

Authors: Faten Ben Bouheni Manish Tewari Andrew Salamon Payson Johnston Kevin Hopkins

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores.

FinTech doi: 10.3390/fintech4030030

Authors: Bashar Abu Khalaf Munirah Sarhan AlQahtani Maryam Saad Al-Naimi Meya Mardini

This study investigates the impact of geopolitical risk, firm characteristics, and macroeconomic variables on the performance of non-financial firms listed on the Egyptian Stock Exchange. The study analyzes a panel dataset consisting of 182 Egyptian firms over the period 2014–2023. Using the panel Generalized Method of Moments (GMM) regression technique, the study examines the effect of geopolitical risk on the return on assets. This study controls for firm characteristics such as liquidity, leverage, and growth opportunities and controls for macroeconomic variables such as inflation and GDP. This empirical evidence investigates the moderating role of FinTech on such relationship. The results reveal a significant and negative relationship between geopolitical risk and firms’ performance. Liquidity, growth opportunities, and inflation show positive and significant impacts. In contrast, leverage and GDP demonstrate significant negative relationships. Remarkably, FinTech moderates the relationship significantly and positively. Therefore, investors ought to proceed with prudence when positioning cash within elevated political volatility. The significant positive moderating effect of FinTech on this connection provides a vital strategic insight: enterprises with enhanced FinTech integration may demonstrate increased resilience to geopolitical shocks.

FinTech doi: 10.3390/fintech4030029

Authors: Bashar Abu Khalaf Munirah Sarhan AlQahtani Maryam Saad Al-Naimi Mohamad Anas Ktit

This study aims to explore how Corporate Digital Responsibility (CDR) influences Jordanian banks’ performance. It focuses on four CDR dimensions—“social, technological, economic, and environmental”—and examines the mediating role of firm size in these relationships. This study is the first to empirically test the mediating effect of firm size in the relationship between CDR and firm performance in the Jordanian banking sector, providing a novel perspective on how digital ethics shape organizational success. Data were collected through a structured survey from 299 bank employees in Jordan. Structural Equation Modeling (SEM) was employed to assess the direct and indirect effects of CDR dimensions on firm performance, with firm size tested as a mediating variable. All four dimensions of CDR significantly and positively affect firm performance. Additionally, firm size plays a partial mediating role in the relationship between CDR and firm performance, indicating that larger banks may better leverage digital responsibility initiatives to enhance performance. The study relies on self-reported data from a single country (Jordan), which may limit generalizability. Future studies could adopt a longitudinal design or expand to other MENA countries for comparative analysis and broader insights. The findings suggest that Jordanian banks should invest in and prioritize CDR strategies, especially in economic and technological domains, to improve their organizational outcomes and stakeholder relationships. Enhancing firm size may amplify the positive impact of CDR. The findings of this study are robust, as validated by further analysis utilizing data from a customer survey. The results derived from customer viewpoints correspond with staff data, substantiating the beneficial influence of Corporate Digital Responsibility (CDR) on banking performance and affirming the substantial mediating effect of company size.

FinTech doi: 10.3390/fintech4030028

Authors: Mindy Joseph Congrong Ouyang Kenneth J. White

This study uses national data to contribute to ongoing discussions regarding social media’s role in influencing investors in the digital economy. Grounded in social network theory, social media engagement was examined for its influence on FinTech usage, specifically cryptocurrency investments, mobile trading applications, and financial podcasts. Results showed a significant relationship between social media use for investment decisions and the embrace of FinTech. Individuals who actively engage with social media for this purpose had higher odds of investing in cryptocurrency and a higher likelihood of using both mobile trading applications and financial podcasts. However, these results were not consistent across all platforms amongst social media users. Our findings show that social media platforms enable peer influence and recommendations through networks that shape financial decisions and behaviors. FinTech firms can strategically harness social ties and the inherent information flows within social networks to broaden their reach and impact in the financial services landscape.

FinTech doi: 10.3390/fintech4030027

Authors: Noah Cheruiyot Mutai Karim Farag Lawrence Ibeh Kaddour Chelabi Nguyen Manh Cuong Olufunke Mercy Popoola

This study applied supervised machine learning algorithms to macro-fiscal panel data from 20 EU member states (2000–2024) to model and predict fiscal stress episodes in the Eurozone. Conventional frameworks for assessing public debt sustainability often rely on static thresholds and linear dynamics, limiting their ability to capture the complex, non-linear interactions in fiscal data. To address this, we implemented logistic regression, support vector machines, and XGBoost classifiers using core fiscal indicators such as debt-to-GDP ratio, primary balance, GDP growth, interest rates, and inflation. The models were evaluated using time-aware cross-validation, with XGBoost delivering the highest predictive accuracy but showing some signs of overfitting. We highlighted the interpretability of logistic regression and applied SHAP values to enhance transparency in the tree-based models. While limited by using annual data, we discuss the potential value of incorporating real-time or high-frequency fiscal indicators. Our results underscore the practical relevance of AI-enhanced early warning systems for fiscal surveillance and support their integration into institutional monitoring frameworks.

FinTech doi: 10.3390/fintech4020026

Authors: János Kálmán

Regulatory sandboxes have emerged as policy instruments designed to support FinTech innovation while maintaining supervisory oversight. By allowing firms to test financial products in controlled environments, sandboxes aim to reduce regulatory uncertainty and facilitate market entry. Despite their growing adoption, empirical evidence of their effectiveness remains limited, particularly in emerging markets. This study explores the impact of regulatory sandboxes on innovation and market access through a qualitative comparative case study of the United Kingdom, Singapore, and Hungary. Drawing on document analysis and thematic coding, the research evaluates sandbox design, regulatory support, and innovation outcomes across the three jurisdictions. Findings show that sandboxes enhance access to funding, accelerate product development, and foster regulator–firm collaboration. While the UK and Singapore benefit from mature ecosystems and structured frameworks, Hungary illustrates sandbox potential in developing markets. The paper contributes to FinTech regulation literature and provides policy recommendations for optimizing sandbox design across varied institutional contexts.

FinTech doi: 10.3390/fintech4020025

Authors: Sylvestre Blanc Emmanuel Fragnière Francesc Naya Nils S. Tuchschmid

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.

FinTech doi: 10.3390/fintech4020024

Authors: Ângela Filipa Oliveira Gonçalves Shafik Faruc Norali Clemens Bechter

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.

FinTech doi: 10.3390/fintech4020023

Authors: Zilong Liu Hongyan Liang

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.

FinTech doi: 10.3390/fintech4020022

Authors: Elysee Nsengiyumva Joseph K. Mung’atu Charles Ruranga

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.