Search Results (569)

Credit risk evaluation is a key factor in financial intermediation, regulatory capital provision, and risk management in the portfolio. In this study, we compare the deep learning performance for probability-of-default (PD) estimation with a structured financial econometric model using loan-level data of an Indian non-banking financial agency between May and August 2025. Using the interpretation of PD as a conditional expectation, which is in line with reduced-form default-intensity models, we compare deep learning, logistic regression, and gradient boosting using a pure time-based out-of-sample design. Model assessment focuses on discrimination and calibration, where the area under the precision–recall curve (AUC-PR), Brier score, log-loss, and Hosmer–Lemeshow goodness-of-fit tests are utilized. The findings show that deep learning achieves higher accuracy in terms of calibration but a lower Brier score by about 18; this gap could be reduced by comparing logistic regression with statistically significant improvements in formal tests that compare forecasts. In portfolio back-testing, better probability scaling is translated into an actual loss reduction of about 12–13% for the August 2025 cohort. Although the improvements compared with the advanced ensemble techniques are moderate, the results indicate that deep learning improves the estimation of conditional default probabilities because of the better nonlinear modeling and upper-tail risk perception. This study contributes to the literature via its incorporation of machine learning and credit risk assessment into a formalized risk management and econometric assessment system. Full article

Mobile payment systems are widely viewed as a practical lever for sustainable financial inclusion in developing economies, with relevance to UN Sustainable Development Goals 1, 8, and 10. Yet in countries such as Egypt—where mobile penetration exceeds 95% but banking penetration remains below 35%—sustained engagement with these services lags policy expectations, suggesting that determinants beyond technology shape behavior. This study examines the determinants of behavioral intention and continued use of mobile payment among Egyptian users, and tests whether cultural factors dominate conventional technology-acceptance predictors in a collectivist, high-power-distance setting. A structured bilingual (Arabic–English) questionnaire measuring nine predictors across technology, psychological, and socio-cultural dimensions was administered to 200 active mobile-payment users in Egypt during January–February 2025. Hierarchical regression and mediation analysis (with Sobel/delta-method 95% confidence intervals as a robustness check) were used to examine direct effects on Behavioral Intention and continued use, and the mediating role of Behavioral Intention. Cultural Influence emerged as the strongest predictor of Behavioral Intention (β = 0.421, p < 0.001), followed by Facilitating Conditions (β = 0.282, p < 0.001); conventional TAM variables were not statistically significant. Cultural Influence retained a significant direct effect on continued use (β = 0.253, p < 0.01), indicating partial mediation. The findings support culture-sensitive approaches to technology adoption research and inform financial-inclusion policy in non-Western contexts. Limitations include the cross-sectional design and the convenience-based snowball sample of existing users. Full article

In the era of global data exchange, banking secrecy is no longer absolute, becoming part of a more transparent tax administration system. International exchange of tax information has necessitated a legal analysis of issues related to tax secrecy and banking secrecy in Kazakhstan. The authors analyse the relationship between banking, tax and official secrecy, as well as international and national mechanisms for protecting confidentiality in the context of growing demands for tax transparency. The article discusses international initiatives, including CRS, FATCA and the Convention on Mutual Administrative Assistance in Tax Matters (OECD), as well as their impact on the legal framework governing financial information in Kazakhstan. Focusing on international standards, the article highlights the lack of legal clarity in Kazakhstani legislation regarding the mechanism for ensuring banking secrecy when transferring information to tax authorities. Measures are proposed to harmonise regulatory acts aimed at ensuring a balance between the confidentiality of taxpayer information and the obligation of banking organisations to assist the tax authority in performing its tax administration tasks, as well as legal certainty in the handling of confidential information. Full article

This article investigates the impact of grassroots cryptocurrency adoption—operationally defined as the intensity of on-chain retail transactions and peer-to-peer (P2P) exchange volumes—on household financial resilience. Utilising aggregate cross-sectional data from 112 countries, we employ a fractional probit regression to account for the bounded nature of our resilience index. The study highlights marked heterogeneity depending on the level of economic development. The results reveal a positive and significant effect in developing countries, whereas a negative association emerges in developed economies. Analysis of the underlying mechanisms indicates a significant moderating role for institutional quality. While cryptocurrency adoption shows a direct positive correlation with financial resilience in emerging markets, its contribution weakens in environments with robust formal institutions. These findings suggest that digital assets primarily function as a substitute for formal financial systems in contexts characterised by institutional voids and limited financial inclusion. Furthermore, the study identifies non-linear relationships between banking penetration and resilience, underscoring the importance of financial system maturity. Overall, the results suggest that cryptocurrency adoption can serve as a functional tool for strengthening worldwide resilience, provided it is supported by targeted regulatory oversight and digital financial education. Full article

Financial fraud has become a critical challenge for modern financial systems due to the rapid growth of digital transactions, online banking services, and electronic payment platforms. Traditional rule-based fraud detection systems are increasingly inadequate in addressing the evolving and adaptive strategies employed by fraudsters. Consequently, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful tools for detecting fraudulent activities in large-scale financial datasets. This paper presents a comprehensive survey of ML/DL approaches for financial fraud detection. The survey systematically reviews existing research across multiple methodological paradigms, including classical supervised learning, anomaly detection, graph-based methods, deep neural networks, multimodal architectures, and cost-sensitive learning frameworks. Particular emphasis is placed on emerging techniques such as graph neural networks, transformer-based architectures, and federated learning approaches designed to address privacy and scalability challenges. In addition to reviewing model architectures, this work analyzes key challenges inherent to fraud detection systems, including extreme class imbalance, concept drift, adversarial behavior, data privacy constraints, and real-time deployment requirements. Furthermore, the survey examines evaluation methodologies, highlighting the limitations of commonly used metrics and discussing more realistic evaluation strategies that incorporate operational costs and risk management considerations. This paper also provides a structured taxonomy of fraud detection methods, comparative analyses of commonly used datasets, and a synthesis of current research trends. Finally, open challenges and promising research directions are identified, including adaptive learning systems, interpretable Artificial Intelligence models, graph-based behavioral modeling, and privacy-preserving collaborative fraud detection frameworks. Full article

Shadow banking poses a significant challenge to China’s financial sustainability. This study examines how city-level regional financial structure influences shadow banking activities among non-financial firms, with implications for building a more sustainable financial system. Exploiting data of Chinese listed firms from 2012 to 2023 and employing fixed-effects regressions with instrumental variable (IV) and dynamic GMM approaches to address endogeneity, the study finds that bank-dominated financial structures significantly reduce corporate shadow banking financing. This effect weakens among financially constrained firms, revealing shadow banking’s role as a gap-filling mechanism, but strengthens when firms exhibit higher digitalization or market attention through enhanced information transparency. These findings suggest that achieving long-term financial sustainability requires regionally nuanced policy interventions rather than uniform regulatory tightening. Instead, policy interventions should be regionally nuanced: expanding formal credit in inland provinces can mitigate financial exclusion, while fostering corporate digitalization helps bridge the information gap between lenders and firms. Furthermore, enhancing market-based oversight is essential to redirecting capital into more transparent and regulated frameworks. Full article

As digital payment infrastructures expand, the incidence of card-not-present fraud has become a major source of operational and financial risk for banks, payment processors, and merchants. In response, financial institutions increasingly rely on data-driven decision systems, yet fraudsters continuously adapt their strategies to evade conventional rule-based controls. A promising way to strengthen risk management is to model transactional data so as to uncover non-trivial, high-dimensional patterns characteristic of fraudulent behavior and to embed these models into real-time decision pipelines. In this work, we develop and compare a suite of learning-based fraud detectors, including a convolutional neural network and several machine learning classifiers, within a unified quantitative risk-management framework. The problem is formulated as a supervised classification task within a quantitative risk management framework, where the cost of missed fraud is particularly critical. The mathematical contribution is methodological rather than architectural: we design a leakage-safe and prevalence-faithful evaluation protocol for extremely imbalanced binary classification, combine cross-validated hyperparameter optimization with risk-aligned model selection based on metrics such as recall and Matthews correlation coefficient, and quantify uncertainty by bootstrap confidence intervals and paired McNemar tests. In addition, we connect statistical evaluation with deployment-time decisioning through a decision-theoretic, cost-sensitive threshold rule, showing how institution-specific false-positive and false-negative costs determine the operating point of the classifier. Because fraudulent transactions constitute only a small proportion of the total volume, we employ resampling strategies to mitigate severe class imbalance and systematically calibrate the models via cross-validated hyperparameter optimization. The empirical analysis on real transaction data shows that carefully tuned deep and ensemble methods can achieve strong fraud-detection performance, while the proposed framework clarifies which performance differences are statistically meaningful and which operating points are most suitable under institution-specific false-positive and false-negative costs. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

Systemic risk remains a key concern for financial authorities, especially in emerging economies where traditional, low-frequency balance sheet indicators often lag rapidly changing market conditions. This study develops a high-frequency Systemic Risk Sentiment Index (SRSI) for the Philippines using news headlines from 2011 to 2025 and an ensemble of domain-specific financial sentiment models. Results show that negative sentiment is mainly driven by external-sector developments, market volatility, and equity-related news, with surges aligning with global and domestic stress episodes. Event study analysis demonstrates that the SRSI captures sharp deteriorations in sentiment several weeks before major financial stress events, while Granger causality results indicate modest predictive power for domestic equity market movements. Overall, the SRSI is best viewed as a responsive, real-time barometer that complements conventional systemic risk measures. This study represents one of the initial efforts to construct a sentiment-based systemic risk indicator tailored to the Philippine financial system and offers a scalable, low-cost framework that other central banks may adopt to enhance real-time macro-financial surveillance. Full article

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms. Full article

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. Full article

Radial distribution feeders are especially sensitive to reactive-power deficits, which increase technical losses, deteriorate voltage profiles, reduce energy efficiency, and indirectly raise the emissions associated with the energy required to supply those losses. In this context, this paper proposes a sustainability-oriented planning methodology for the location and sizing of capacitor banks in radial distribution feeders, aimed at jointly improving technical performance, economic viability, and sustainability-related energy benefits. The problem is formulated as a discrete multi-objective model and solved through a constructive Greedy heuristic combined with backward/forward sweep load-flow evaluation, considering commercially available capacitor sizes. The methodology is validated on the IEEE 34-bus feeder, a demanding benchmark that remains less frequently used than the IEEE 33- and 69-bus systems in recent capacitor-planning studies. Seven scenarios are analyzed, from the uncompensated base case to configurations with up to six capacitor banks. The results show that all compensated scenarios improve feeder performance, reducing active losses from 25.3327 kW to a minimum of 20.1468 kW, equivalent to a maximum reduction of 20.47%, and increasing the minimum nodal voltage from 0.95528 p.u. to 0.97038 p.u. From a purely financial perspective, the one-bank scenario yields the highest net present value (USD 16,358.86), whereas the two-bank scenario emerges as the most balanced solution within the evaluated set, with annual savings of USD 5432.29 and a net present value of USD 11,497.58. Overall, the results confirm that capacitor-bank planning should be addressed as a trade-off among electrical efficiency, voltage support, profitability, and sustainability-oriented benefits. The proposed framework provides a simple, reproducible, and interpretable planning tool for radial distribution feeders. Full article

The stability of Indonesia’s banking sector is closely linked to the effectiveness of capital regulations, particularly as a country that aligns its policies with Basel III standards. Ensuring that banks have adequate capital buffers is crucial for mitigating systemic risk. However, the interaction between regulatory requirements and actual banking behavior in developing countries remains poorly understood. This study aims to evaluate the impact of Indonesia’s capital requirement instruments, including the countercyclical capital buffer (CCyB), the capital conservation buffer (CCB), and the capital surcharge, on credit performance and financial stability across various bank categories. Using a quantitative approach, the analysis utilizes panel data from commercial banks, state-owned banks and regional development banks over several periods, using the panel regression method and Difference-in-Differences (DID) to assess how changes in buffer levels affect credit growth, Non-Performing Loans (NPLs), and the Capital Adequacy Ratio (CAR). The results show that capital buffers have a statistically significant effect on lending behavior: a 1% increase in buffer levels is associated with a measurable decrease in credit expansion across several bank groups, while CCBs exhibit a stronger stabilizing effect than CCyBs. Although these instruments do not eliminate financial uncertainty, they contribute to more prudent risk-taking. This study also revealed that the CCyB rate increases when the financial cycle is in an expansionary phase. Conversely, if the economy slows (as during the pandemic), the CCyB rate can be lowered back to 0% to encourage bank intermediation, thus shaping the bank’s responses to regulation. Several implications of implementing a capital buffer in Indonesia include the benefits of resilience and bank behavior during credit expansion. Overall, this study concludes that aligning regulatory frameworks with real-world banking behavior is crucial for enhancing financial stability in developing countries, such as Indonesia. Full article

This study examines the economic consequences associated with the mandatory adoption of International Financial Reporting Standards (IFRS) in the Iraqi banking sector. Motivated by growing evidence that the outcomes of IFRS adoption depend on institutional and market conditions, the study focuses on a bank-based emerging economy characterized by relatively underdeveloped capital markets and evolving enforcement mechanisms. Using a balanced panel of 24 banks listed on the Iraq Stock Exchange over the period 2014–2018, the analysis exploits the mandatory IFRS adoption in 2016 within a before–after regulatory framework. Panel regression techniques are employed to examine the associations between IFRS adoption and stock market liquidity, firm value, information asymmetry, and the cost of debt, while controlling for bank-specific characteristics and macroeconomic conditions. The results indicate that IFRS adoption is positively significantly associated with stock market liquidity, and negatively significantly associated with information asymmetry, consistent with improvements in the informational environment of Iraqi banks following enhanced disclosure and comparability. The findings also reveal a positive and significant relationship between IFRS adoption and the cost of debt, suggesting higher perceived financial risk by creditors. In contrast, no statistically significant association is observed between IFRS adoption and bank market valuation, highlighting the limited sensitivity of equity prices to accounting reforms in thin and institutionally constrained markets. Overall, the study contributes to the literature on the economic consequences of IFRS adoption by providing evidence from an underexplored emerging market and a highly regulated banking sector. The findings underscore the role of institutional context in shaping the outcomes of accounting standard convergence and offer policy-relevant insights for regulators and standard-setters in bank-oriented financial systems. Full article

This research enhances the literature on bank capital structure by combining financial intermediation theory with technological innovation to analyse the impact of FinTech adoption and liquidity management on leverage choices in South African banks. Utilising panel data spanning 2015 to 2024 and applying the Generalised Method of Moments (GMM) to tackle endogeneity and dynamic persistence, the research presents new findings from an overlooked emerging market setting. The results show a diverse effect of technology on leverage. Conventional banking systems, represented by automated teller machines (ATMs), show a positive relationship with the total debt ratio (TDR), suggesting a capital-intensive nature of tangible assets. Conversely, digital technologies such as mobile banking and a composite FinTech Index display a notable negative correlation with leverage, indicating that digital transformation improves efficiency, strengthens internal funding capacity, and reduces dependence on external debt. Moreover, increased liquidity levels are negatively correlated with leverage, suggesting that well-capitalised banks with robust liquidity rely less on debt funding. By examining FinTech and liquidity dynamics, the research contributes to both theory and practice, emphasising digital innovation as an alternative to external funding and stressing the importance of sound liquidity management amid evolving regulatory environments such as Basel III. Full article