The Economics and Finance Letters

Exploring fintech adoption in cashless transactions: Evidence from the bank for investment and development of Vietnam

2025-12-11T23:55:31-06:00

In the era of Industry 4.0 and ongoing digital transformation in the banking sector, Fintech has become a vital catalyst for the growth of cashless payment services in Vietnamese commercial banks. The Bank for Investment and Development of Vietnam (BIDV) is among the leading banks at the forefront of Fintech adoption, aiming to improve customer experience and enhance the efficiency of electronic transactions. However, the extent of Fintech adoption varies significantly across BIDV branches and is influenced by various factors. Grounded in theoretical foundations and a comprehensive review of previous studies, this research develops a conceptual model to identify the main factors affecting the level of Fintech adoption in cashless payments at BIDV. Primary data were collected through a structured survey of 250 customers across multiple BIDV branches. The data were analyzed using SPSS software, beginning with reliability testing to confirm the consistency of measurement scales. Subsequently, exploratory factor analysis (EFA) was employed to uncover the underlying factor structure, followed by multiple regression analysis to assess the impact of each factor on Fintech adoption levels. The results indicate that all identified factors have a positive and statistically significant influence on the degree of Fintech adoption across BIDV branches. Based on these findings, the study offers several practical recommendations to promote more effective and consistent Fintech implementation. The main contribution of this research lies in providing empirical evidence and a validated analytical framework for understanding the key drivers of Fintech adoption within Vietnamese commercial banking.

Examining the connection of money laundering and environmental crime: A deep dive into dirty money and trade

2025-12-16T03:29:10-06:00

This study explores current research trends on Trade-Based Money Laundering (TBML) within the context of environmental crime through a structured review of academic literature. It then investigates the association between TBML and environmental crime incidents across Southeast Asia. A two-stage methodology is adopted. The first stage systematically reviews studies published between 2006 and 2025, revealing that TBML research remains in its infancy and is principally focused on conceptual frameworks with limited empirical validation. The second stage applies a fixed-effects panel regression, using trade mis invoicing as a proxy for TBML and controlling for corruption, government intervention, and cross-border mobility. The results indicate a positive and statistically significant association between TBML and environmental crime, with the relationship strengthened in jurisdictions marked by higher levels of corruption and weaker enforcement. These findings underline the necessity for coordinated regional action and a holistic policy response to all TBML dimensions. Policymakers should conduct a comprehensive review of Southeast Asian regulatory frameworks to close loopholes, strengthen cross-border cooperation, and deploy real-time fraud detection systems to enhance transparency and deter illicit conduct. Enforcement must remain proportionate to avoid displacing laundering into clandestine channels, striking a balance between vigilant oversight and the facilitation of legitimate trade.

Fintech's powerful function: Reducing gender income inequality

2025-12-25T05:34:40-06:00

The innovation of financial technology (fintech) has significantly transformed the global financial landscape. Fintech's impact is particularly crucial for alleviating income inequality, as women facing gender-specific financial barriers experience enhanced economic opportunities through algorithm-based lending decisions and expanded digital access. Concurrently, the income disparity between men and women persists as a significant global challenge that researchers have extensively investigated. This study examines the relationship between fintech adoption and gender income inequality by analyzing provincial panel data from China spanning 2011 to 2021. Employing both Ordinary Least Squares (OLS) and Two-step Generalized Method of Moments (GMM) models, our findings reveal that fintech adoption measured by the breadth and depth of digital finance use and the level of financial inclusion digitization significantly reduces gender income inequality. This study provides a novel analysis demonstrating a significant negative relationship between fintech adoption and gender income inequality at the provincial level across all dimensions: the breadth, depth, and digitization of fintech. This suggests that Fintech serves not only as a transformative tool for the financial industry but also contributes significantly to socioeconomic equity. By evaluating the impact of digital financial services on gender economic outcomes, this research provides valuable implications to policymakers, financial institutions, and Fintech product developers promoting fintech and gender equality. Based on these findings, we recommend promoting fintech as an effective tool for advancing gender pay equity.

The effect of leadership style on return on equity classification: An artificial neural network approach

2025-12-26T22:53:02-06:00

This study investigates whether specific board-level leadership behaviors, particularly decision-rigour and strategic monitoring, can predict Return on Equity (ROE) tiers in Ghanaian banks. Addressing a gap in emerging market governance research, it integrates Upper-Echelon Theory and the Resource-Based View to explore behavioral influences on financial performance. Using a mixed-methods approach, data were collected via a 15-item Board-Efficacy Scorecard (BES) across ten banks and combined with 2023 ROE figures, categorized into Low, Medium, and High tiers. An Artificial Neural Network (ANN) model with two hidden layers (15 neurons each) was trained and benchmarked against multinomial logistic regression and random forest classifiers. The ANN achieved superior predictive accuracy (AUC = 0.94), significantly outperforming traditional models. Sensitivity analysis revealed that decision-rigour and strategic monitoring collectively increased the probability of High-ROE classification by 22 percentage points. These findings validate both the BES tool and the use of ANN in leadership-performance research. The study proposes the Board-Analytics and Disclosure Directive (BADD), advocating for BES publication, AI-powered board dashboards, and performance-linked compensation for directors. These measures aim to enhance governance transparency and financial outcomes in emerging markets. This is the first empirical application of ANN for ROE classification in Ghana's banking sector. By integrating advanced machine learning with context-sensitive behavioral metrics, the study demonstrates how board dynamics can be transformed into strategic tools for improving firm performance.

The impact of environmental tax, economic growth and renewable energy on CO₂ emissions in Vietnam: Evidence from a VECM approach

2025-12-26T23:38:20-06:00

The research assesses the impact of environmental taxes, economic growth, and renewable energy consumption on CO₂ emissions in Vietnam during the period 2001-2023. Based on the externality theory, the Environmental Kuznets Curve, and sustainable development theory, the study constructs a Vector Error Correction Model to analyze the dynamic relationships between variables in both the short and long term. The test results indicate that all data series are stationary after first-order differences, with between one and four cointegration relationships among the variables. Empirical analysis reveals that, in the long run, environmental taxes and renewable energy consumption have positive and statistically significant effects on CO₂ emission control, whereas GDP per capita growth exhibits a negative and strong effect. In the short term, only environmental taxes demonstrate an immediate regulatory effect, while GDP and renewable energy do not clearly influence emission control. The error correction coefficient is negative and significant, confirming the existence of a long-term equilibrium adjustment mechanism. Additionally, Granger causality tests show that environmental taxes and economic growth are drivers of CO₂ emission behavior. Variance decomposition highlights the dominant role of GDP, while the impact of renewable energy remains limited. Based on these empirical findings, the study proposes policy implications to enhance the effectiveness of tax instruments, promote green investments, expand renewable energy use, and improve policy coordination to control CO₂ emissions, thereby supporting sustainable economic development in Vietnam.

The impact of audit quality on operational efficiency of non-financial enterprises listed on the Vietnamese stock market in the period before and after COVID-19

2025-12-27T01:04:37-06:00

This research seeks to examine the effect of audit quality on the financial performance of non-financial listed firms in Vietnam during the period 2014–2023, with a sub-period analysis of pre- and post-COVID-19 outbreak. It considers major determinants of audit quality, including Big 4 auditors and audit rotation, as well as financial variables such as leverage, size, and industry type. The results indicate that companies audited by the Big 4 tend to have better financial performance, which is attributed to higher transparency and credibility in financial reporting. Nonetheless, the positive relationship was weakened during the COVID-19 period, as increased economic uncertainty limited the contribution of audit quality to maintaining firm value. Most importantly, the study concludes that audit rotation, measured by the frequency of auditor changes, has no statistically significant impact on financial performance. This suggests that the length of auditor rotation, within the current regulatory context, may not significantly affect audit quality and corporate performance. Furthermore, financial leverage consistently has a negative effect on firm value, highlighting the importance of effective capital structure management. These findings underscore the need to reinforce audit quality standards and reconsider audit rotation policies to enhance auditor independence, ensure continuity, and develop auditor expertise. Such measures are essential to promote greater financial transparency, restore investor confidence, and improve efficiency amid economic turmoil.

Regulatory reinforcement, audit effort, and market perceptions of restatements

2025-12-27T01:31:53-06:00

This study examines the changes in audit effort and financial restatements following the November 2018 amendment to the External Audit Act in South Korea. Using 2016–2022 as the sample period, it investigates whether restatements differentially affect the value relevance of earnings in the capital market. The findings indicate that the strengthened regulatory framework significantly increased audit effort, as measured by audit hours, total audit fees, and audit fees per hour, which, in turn, increased restatements. Before the External Audit Act was amended, the market interpreted restatements as negative signals, thereby reducing the value relevance of earnings. However, following regulatory reinforcement, such negative perceptions diminished, and this effect was more pronounced in firms with higher audit effort. These results suggest that investors distinguish between restatements stemming from managerial misconduct and those arising from enhanced audit scrutiny in a stricter regulatory environment. By highlighting the mediating role of increased audit effort in the relationship between regulatory reinforcement and financial restatements, this study provides insights into factors that influence the perceived reliability of financial disclosures. It offers implications for accounting standard-setters and policymakers concerned with the broader effects of strengthened regulations on audit practices and market perceptions.

Estimating value at risk in Saudi real estate investment trusts: A garch-based approach

2025-12-27T04:18:30-06:00

This paper compares the performance of the GARCH (1,1) and GJR-GARCH (1,1) models in forecasting VaR for Saudi REITs across three distinct periods: the pre-COVID period (2016–2019), the during-COVID period (2020–2021), and the post-COVID period (2022–2024). The study estimates log returns and models them using GARCH-type structures, applying the Kupiec test for backtesting the VaR forecasts. The results show that both GARCH (1,1) and GJR-GARCH (1,1) models are effective in predicting risk across all three periods. However, statistical model comparison indicates that the GJR-GARCH (1,1) model outperforms the GARCH (1,1) model consistently across all periods. Nevertheless, its advantage is most pronounced during the COVID-19 period, when extreme market turbulence and asymmetric volatility were present. These results support the necessity of solid volatility modeling regarding REIT risk management, particularly in emerging markets and under both normal and extreme market conditions. This study makes a novel contribution by being the first to apply and compare GARCH-type models specifically to the Saudi REIT market across these pandemic-defined subperiods. It addresses a gap in regional volatility modeling and demonstrates the superior performance of asymmetric models under crisis conditions, offering insights for REIT risk management in emerging markets.

Financial inclusion, competition and banking stability: Evidence from MENA countries

2025-12-27T05:12:47-06:00

Prior studies have shown in the finance industry that bank stability and financial inclusion are related, but the study on the role of the financial sector in influencing this relationship remains underexplored. This study aims to determine whether increased access to financial services leads to greater stability or introduces risks to the concentrated banking market. The research examines panel data from 96 banks across 13 Middle Eastern and North African (MENA) countries between 2010 and 2022. The empirical methods employed include the system Generalized Method of Moments (GMM) and fixed-effects panel regressions. Principal component analysis (PCA) is used to develop a composite financial inclusion index that considers access, consumption, and depth. The findings indicate that financial inclusion, particularly the access component, has the potential to enhance bank stability. However, this stabilizing effect is less pronounced and may even invert under conditions of high market power of banks. These negative impacts are most significant in countries within the lowest quartile of financial inclusion, suggesting that increasing access to monetary services could inadvertently lead to systemic risks in less competitive banking systems. These results highlight the importance of aligning financial inclusion policies with measures that promote competition. For policymakers in the MENA region, the optimal approach to improving access involves offering services through branches, ATMs, and digital channels, complemented by regulatory reforms aimed at ensuring stability without introducing additional risks.

Determinants of credit risk in lower-middle-income countries: Evidence from interest spread, efficiency, and macroeconomic factors

2025-12-27T05:42:20-06:00

The study examines the influence of interest rate spread, efficiency, and macroeconomic factors on credit risk by analyzing a sample of lower-middle-income countries (LMICs) worldwide. It utilizes secondary data from 25 LMICs over the period 2000-2021 and employs the Pooled Ordinary Least Squares (POLS), Fixed-Effect (FE), and Random-Effect (RE) estimators. The baseline model indicates that the interest spread increases credit risk in LMICs. This finding is supported by sensitivity analysis. Additionally, the results reveal that inefficient banks produce higher non-performing loans (NPLs) in LMICs. Conversely, an increase in the capital ratio reduces NPLs, a result confirmed by sensitivity analysis. The presence of excess liquidity and minimal competition contribute to the rise of NPLs in LMICs, with further validation from the consistency check. Economic growth is associated with a reduction in credit risk faced by banks in LMICs. The relationship between inflation and credit risk remains inconclusive. Policymakers and regulators can utilize these findings to implement effective corrective measures before banks become insolvent due to high NPLs, thereby protecting banks from bankruptcy and safeguarding the real economy from shocks.

Integrating corporate social responsibility dimensions and product innovation in firms in the Ghanaian insurance industry

2025-12-27T07:19:11-06:00

This study evaluates how Corporate Social Responsibility (CSR) dimensions affect product innovation performance in Ghanaian insurance companies. The study clarifies which dimensions of CSR (i.e. Economic, Legal, Ethical, and Philanthropic Responsibilities) are most influential in driving innovation performance and the effect of their combined interactions. Survey data of 389 employees from 49 insurance companies in Ghana were analyzed using Partial Least Squares Structural Equation Modelling (SEM). SEM was conducted after confirmatory factor analysis confirmed the reliability and validity of the constructs. Bias-corrected bootstrapping was used to assess the direct and moderated path relationships. The results showed that Economic CSR (β = 0.341, p < 0.05), Legal CSR (β = 0.143, p < 0.05), and Ethical CSR (β = 0.219, p < 0.05) were positive determinants of insurance product innovation performance. Economic CSR × Philanthropic CSR → Product Innovation (β = 0.113, p < 0.05) and Legal CSR × Ethical CSR → Product Innovation (β = 0.177, p < 0.05) were significant moderating relationships. The other four-way interactions were non-significant. The result shows that, although Economic CSR, Legal CSR, and Ethical CSR individually affect product innovation, their interactions produce positive outcomes for insurance companies. The combined synergies arising from the interactions create environments that enhance the positive outcomes of product innovation over single impacts. This implies that firms should craft their CSR strategies to exploit cross-dimensional complementarities. Focusing on integrated CSR dimensional strategies can unlock greater innovative capacity in the insurance industry.

Gold price volatility, stock market, and inflation in Turkey: An ARDL approach

2025-12-27T07:47:21-06:00

This study aims to examine how anticipated gold price volatility affects Turkey’s stock prices and inflation through two distinct models that use Turkey’s country-specific economic uncertainty, industrial output index, and interest rate as control variables. For this purpose, the study first employs the GARCH technique to forecast a conditional variance series, which reveals the volatility of the gold price to reflect the underlying uncertainty and instability within financial markets. Second, by employing this new series, the ARDL approach is used to test the effect of gold price volatility on BIST100 and inflation in Turkey. According to the ARDL results, all variables in Model 1 have significant short- and long-term coefficients. All variables except inflation harm the BIST100 index over time. Additionally, the results suggest that volatility and uncertainty increase inflation in Model 2, while lagged interest rates have a significant impact on inflation, indicating that both internal and external shocks impact inflation and stock prices. These results underscore the transmission of both internal and external shocks to the financial system and pricing dynamics. The findings of the paper emphasize that, in an emerging economy like Turkey, marked by structural weaknesses and heightened susceptibility to global uncertainty, gold price volatility is not only a financial occurrence but a vital factor influencing macroeconomic stability and policy efficacy. Furthermore, external shocks highlight the limited effectiveness of monetary interventions in achieving disinflation objectives.

Digital currency and its implications for traditional banking systems: A qualitative inquiry

2025-12-27T08:50:32-06:00

The purpose of this study is to examine the transformative implications of digital currencies, with a particular focus on cryptocurrencies and central bank digital currencies (CBDCs), for the conventional banking system. The analysis aims to understand how these instruments affect banking operations, consumer trust, regulatory frameworks, and overall financial stability. The study applies a qualitative content analysis to a broad collection of peer-reviewed articles, policy documents, and industry reports. This approach makes it possible to identify recurring themes related to operational impacts on banks, adoption drivers, regulatory and compliance challenges, and macro-level stability concerns. The findings indicate that digital currencies are reshaping bank margins, raising the threat of disintermediation, and fueling shifts in consumer confidence, while regulatory design and policy choices remain critical for safeguarding stability. The evidence shows that the adoption of cryptocurrencies and CBDCs is not only influencing how banks manage payments and deposits but also altering the regulatory environment and competition within financial services. The practical implications of this research suggest that banks must enhance risk management practices, diversify strategies, and adopt a more balanced regulatory approach to survive in the evolving digital financial ecosystem. The study integrates fragmented evidence into a coherent framework that both contributes to theoretical understanding and provides policy recommendations for regulators and practitioners.

A machine learning approach for credit risk assessment of SMEs: Evidence from Morocco

2025-12-27T09:20:36-06:00

Assessing the credit risk of small and medium-sized enterprises (SMEs) has become increasingly complex as borrower profiles are diverse and often non-linear. Traditional rating methods, still widely used in practice, struggle to capture this variability, which can limit their reliability in modern financial contexts. The objective of this study is to evaluate whether machine-learning techniques can provide more accurate and operationally useful tools for SME credit-risk assessment. Using a dataset of 124 Moroccan SMEs, containing financial, behavioral, and transactional variables, we applied three supervised classification models: logistic regression, random forest, and XGBoost, to predict contract defaults. The models are assessed through standard performance metrics including accuracy, precision, recall, F1-score, and AUC. Results demonstrate that XGBoost provides the strongest detection of defaults, eliminating false negatives in our test set and making it especially suitable for loss-minimization contexts. By contrast, random forest achieves the highest discrimination between risky and non-risky profiles (AUC = 0.93), offering a balanced solution for operational scoring. Logistic regression, while less accurate, retains value for its interpretability and transparency. Overall, the findings highlight that ensemble methods, particularly XGBoost, can significantly improve the reliability of SME credit-risk evaluation. These results provide practical insights for financial institutions seeking to minimize default risk while also advancing the integration of artificial intelligence into credit-risk management frameworks.

Anti corruption mechanisms and public finance management efficiency in Ukraine’s war and post-war recovery

2025-12-28T07:04:43-06:00

This report analyzes the potential contribution of anti-corruption measures to improving Public Financial Management as part of Ukraine’s anticipated post-war reconstruction. It draws on several comparative case studies from countries that have emerged from conflict, including Croatia, Georgia, Hungary, Bosnia and Herzegovina, and Rwanda. Although Ukraine remains in a state of war, this article adopts a forward-looking perspective and applies insights from post-conflict economies to explore possible policy implications. The primary objective is to highlight the role of anti-corruption efforts, institutional reform, and transparency in enhancing fiscal governance within fragile contexts. The study employs a panel dataset covering six conflict-affected economies for the period 2020-2025. It applies econometric methods, including cross-sectional dependence tests, CADF and CIPS unit root analysis, Fixed Effects estimation, and Two-Stage Least Squares (2SLS) estimation, to examine the relationships between governance reforms and PFM outcomes. Results show that anti-corruption measures, such as the establishment of independent agencies and the implementation of e-procurement systems, contribute positively to fiscal transparency and budget discipline. Institutional soundness, along with the supportive role of foreign aid and international institutions, also emerges as a significant factor. There are precise policy insights for Ukraine. The study offers a meaningful forward-looking roadmap, grounded in empirical evidence from comparable post-conflict countries, outlining effective strategies for achieving sound public financial management during the reconstruction period.