FinTech, Volume 4, Issue 3 (September 2025) – 25 articles

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. Full article

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 [...] Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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”. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article