Abstract: In emerging economies characterized by a predominance of the banking sector, the trans-mission of monetary policy to bank credit remains a central and ongoing topic of debate. Although the interest rate channel is the primary tool of central banks, numerous studies reveal persistent inertia in short-term bank credit, casting doubt on the effectiveness of monetary transmission. This study examines the transmission of monetary policy to bank credit for non-financial businesses in Morocco, adopting a dynamic, long-term approach. The empirical analysis is based on monthly data covering 2006–2023 and uses an ARDL–ECM model that distinguishes short-term dynamics from long-term adjustment mecha-nisms and incorporates structural breaks. The results indicate that variations in the policy rate do not have a significant effect on short-term bank credit, which confirms the weaken-ing of the traditional rate channel. However, this inertia is accompanied by a strong long-term equilibrium relationship between credit, monetary policy, and risk conditions. The results highlight a gradual monetary transmission, strongly influenced by credit risk and bank balance-sheet arbitrage. The apparent inefficiency of the short-term rate channel thus reflects a transmission modulated by prudential and structural constraints, rather than a breakdown of the monetary transmission mechanism.

Abstract: Small and Medium Enterprises (SMEs) must manage their cash flow effectively to survive and grow. However, many SMEs continue to experience cash flow challenges that can lead to operational disruptions or business failure, particularly in developing economies where resources and financial management capacity are often limited. This study sought to examine existing cash flow management strategies in addressing poor practices by SMEs. The study employed a qualitative approach to examine types of cash flow management strategies used by SME owners and how well they work to solve typical cash flow problems across different global contexts, including South Africa. Methodically, a systematic literature review (SLR) was conducted. The review searched scholarly databases for peer-reviewed papers that were published only in English between 1996 and 2025, on existing characteristics of SMEs’ cash flow management strategies. A purposive sampling method was applied through the population, intervention, comparison, outcome, and context (PICOC) framework, which guided the eligibility and selection of studies. The final sample size for this study consisted of 24 peer-reviewed articles that met all inclusion criteria. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram was used to summarize the process of selecting studies. Overall, the study concludes that although effective strategies are available, their success depends on proper implementation supported by adequate training, accessible financial systems, and stronger operational discipline. These findings highlight key areas where targeted interventions could enhance SME liquidity and improve overall financial sustainability.

Abstract: This study proposes a two-stage structural model that integrates financial literacy, educa-tion, attitudes, knowledge, behavior, advice, and financial stress as predictors of financial capabilities. It also examines the relationship between financial capabilities and financial well-being, highlighting financial resilience as a mediator. The main contribution is posi-tioning financial resilience as a central explanatory mechanism, challenging previous linear approaches. This holistic perspective addresses theoretical gaps and provides em-pirical evidence in an emerging economy context. A non-experimental, quantitative, cross-sectional design was used with a sample of 365 university students from Veracruz, Mexico. Data were collected through an online questionnaire and analyzed using explor-atory and confirmatory factor analysis, structural equation modeling (SEM), and media-tion analysis with bootstrap processing. Results show that financial literacy, education, attitude, advice, and behavior positively impact financial capabilities, with advice being the most significant predictor. Financial capabilities have a strong influence on financial well-being, while financial resilience does not act as a mediator. Study limitations include its cross-sectional design and non-probability sample, limiting generalizability. Future research could explore additional mediators and moderators and evaluate interventions tailored to different socio-economic contexts.

Abstract: Research on forecasting corporate financial performance has rushed from traditional econometric models toward machine learning, deep learning, and high-precision hybrid AI architectures. These methods can capture nonlinear relationships, high-dimensional structures, and regime shifts in financial data more effectively, which has driven their widespread adoption. At the same time, practical requirements for interpretability, regulatory transparency, and model risk governance have made explainable AI an essential component of modern forecasting systems. This Structured Literature Review synthesizes ninety-three empirical studies published between 2000 and 2025 using a PRISMA-informed selection procedure. It evaluates the actual contributions of hybrid AI and explainable AI to corporate financial performance forecasting. The review shows that econometric and machine learning hybrids, ensemble learning models, DEA-based machine learning frameworks, deep learning combined with signal processing, and multimodal architectures are extensively used and collectively improve predictive accuracy and stability. Methods such as SHAP, LIME, partial dependence, and individual conditional effect analyses, attention mechanisms, and counterfactual reasoning significantly enhance model interpretability, support managerial decision-making, and strengthen compliance with regulatory expectations. Despite these advances, challenges remain, including the predominance of static data analysis, limited generalizability, and the lack of architectures designed for realistic deployment. Future research should focus on multimodal data integration, causal AI, adaptive, real-time learning frameworks, and explainable hybrid systems aligned with regulatory and governance requirements.

Abstract: Although the number of Islamic mutual funds (IMFs) in Indonesia has grown in recent years, the market remains relatively small compared with countries such as Malaysia and Saudi Arabia. This study explores the key drivers and barriers influencing IMF development and Indonesian investors’ attitudes toward these products. Drawing on 21 semi-structured interviews with investment managers, regulators, and policymakers, the study identifies major constraints, including regulatory complexity, a limited and illiquid pool of Shariah-compliant securities, suboptimal fund performance, tax limitations, low product awareness, limited Islamic financial literacy, and moderate investor risk orientation. Simultaneously, several factors support potential growth, such as robust investor protection regulations, a large and youthful population, opportunities for product innovation, and strong government backing. The findings offer practical guidance for enhancing product offerings, improving the regulatory framework, and promoting financial literacy, while underscoring that fund performance, investor awareness, and financial literacy remain central determinants of investment behavior.

Abstract: Over the past few years, the banking sector has undergone a rapid digital transformation. The combination of AI and ML is disrupting the old age systems and rule-based systems like anything. Various sectors are benefiting from implementing Artificial Intelligence (AI) and Machine Learning (ML) technologies to automate, personalise, and predict further changes in the customary banking model. In this paper, the study analyses the role of Machine Learning in improving customer experience and managing risk in the banking industry. Additionally, this paper discusses recent banking use-cases for chatbots, credit scoring systems, fraud detection systems, and anti-money laundering systems built using AI and ML. It also covers the ethical and regulatory aspects of AI and ML in the banking industry, including the concerns of privacy, algorithmic accountability, and algorithmic transparency. The report ends with a brief description of new technologies such as Explainable AI, Quantum Computing, and the use of Blockchain technology in financial systems. With the help of Artificial Intelligence and ML, customer experience has continued to improve, and risk management in the financial system is getting better. This study adopts a conceptual and literature-based analytical approach, and also makes an attempt to draw insights from recent empirical studies and industry reports are used to synthesise the applications of AI and ML in modern banking. The paper contributes by integrating dual perspectives, viz., customer experience and risk management, into a unified analytical framework. This study provides a structured understanding of how AI and ML combinedly enhance banking operations, offering theoretical and managerial insights for future empirical research.

Abstract: This study examined the relationship between perceptions of financial health indica-tors, lived experiences, and actions taken to address economic crises, while also ex-ploring potential gender differences. A non-experimental, quantitative, cross-sectional design was applied to a sample of 499 working professionals who had graduated from universities in Veracruz and were employed in either the public or private sector. A 24-item Likert-scale instrument was used to assess financial health perceptions, per-sonal experiences, and crisis-related actions. Reliability was confirmed with Cronbach’s alpha and McDonald’s omega values above 0.7. Data were analyzed using Exploratory Factor Analysis with Varimax rotation, Structural Equation Modeling, and Bayesian analysis to assess gender differences. A four-factor structure explained 64.86% of the variance. Moderate correlation was observed between financial wellbe-ing and resilience (r = 0.32), a weaker correlation between wellbeing and experiences (r = 0.18), and a strong correlation between experiences and actions in crises (r = 0.47). No significant gender differences were found. Findings highlight strategies for man-aging financial crises, maintaining credit health, and improving resilience, proposing a refined three-factor model linking experiences and actions to financial outcomes.

Abstract: This paper investigates the behavioral bases supporting real estate investment choices within the newly developing European market through the analysis of EU-accession optimism-driven cognitive and social heuristic activation. Based on the cross-sectional survey design using data collected from 462 respondents within the Vlora region in Albania, this study uses the PLS-SEM model, including hypotheses that examine the mediating and moderating effects of anchoring, overconfidence, and herding. Results indicated that the positive direct effect of EU-related optimism on investment intention is significant (β = 0.42, p < 0.001), as well as that on price expectations (β = 0.37, p < 0.01). The partial mediation effect of anchoring between optimism and investment intention is significant (β = 0.31, p < 0.05), which is additionally strengthened by overconfidence and herding. The multi-group test further confirmed the presence of significant behavioral divergence among locals, diaspora Albanians, Kosovo Albanians, and European visitors, underlining the crucial influence of informational distance and external reference pricing on bias creation. By treating the process of EU accession as a sentiment-based process rather than a strictly macroeconomic process, this study seeks to make a contribution to the field of behavioral finance related to real assets.

Abstract: Central banks around the globe are rapidly progressing towards digital currency. However, its adoption rate has been consistently low among both emerging and advanced economies. This study examines the user adoption of Indian digital currency, e₹ based on primary survey conducted between July 2025 to September 2025 of 751 respondents. The study adopted TAM for the first time in the digital currency domain and the study stands novel in blending the nudge theory with stated preference method in finance literature to understand the willingness to shift to e₹ in India. Using binary logit regression, we test two hypotheses. Result show that apart of socio-economic predictors, adoption of e₹ is significantly influenced by digital financial literacy. With respect to willingness to shift to e₹, the study found TAM constructs like perceived convenience, perceived belief in the study as the key predictors. Unlike the current literature, our study finds that, trust is not a significant predictor in e₹ adoption. The findings highlight the importance of digital financial literacy and behavioral intensions, apart from technical viability, as the key factors in digital currency adoption in India.

Abstract: This paper looks at an emergency lending scheme offered by Germany’s national development bank (NDB) KfW during the Covid crisis. We analyze the design of the KfW scheme and identify obstacles to efficient contracting in these two-tier lending relationships, involving the NDB, the participating commercial banks, and the ultimate firm borrowers. Theoretical arguments and empirical evidence help to understand the main risks of subsidized public lending schemes. We propose a smart set of public lending contracts which induces banks to refrain from applying for public support if firms don’t need the funds because they re financially strong, or if they don’t deserve the funds because they are zombie firms. In the lending scheme, a firm chooses the contract which maximizes the subsidy, reveals its rating, and obtains public funds according to its crisis-induced needs thereby mitigating information asymmetries. In order to ensure incentive alignment, banks retain a share on borrower default risk. This co-insurance component should increase with crisis duration in order to contain moral hazard, i.e. zombification risk.

Abstract: Africa has the youngest population worldwide, with many young people engaged in informal or temporary employment. Long-term financial resilience in this demographic requires that they develop strong digital financial literacy (DFL) skills, including saving, investing, and managing risk through digital platforms. This study investigates digital financial literacy (DFL) among 300 Rwandan young adults aged 18–32 years and explores an AI-enabled intervention, aligning with Sustainable Development Goals (SDGs) 1 (No Poverty), 5 (Gender Equality), 8 (DecentWork and Economic Growth), 9 (Industry, Innovation and Infrastructure), and 10 (Reduced Inequalities). Findings reveal average financial knowledge, moderate digital literacy, and engagement in budgeting and saving behaviors, but persistent gaps in access to formal financial services and cybersecurity practices. Significant gender disparities were identified, with men demonstrating higher financial knowledge and participation in savings and investments, and higher educational attainment was positively associated with DFL.The low-fidelity chatbot intervention for loan literacy, delivered via a mobile money platform—designed based on survey insights—showed limited usability and acceptability due to participants’ low awareness of personal finances and prolonged task times. These results highlight the need for inclusive, context-sensitive digital financial education solutions and responsible AI integration within digital financial ecosystems to advance sustainable financial inclusion and economic empowerment in low-resource settings.

Abstract:

The networked nature of interbank connections creates vulnerability to systemic risk, which arises from inter-dependencies caused by common asset holdings when faced with exogenous negative shocks. This paper employs Exponential Random Graph Models (ERGMs) to reconstruct the network system of asset-holding correlations from the balance sheets of Chinese commercial banks from 2016 to 2022. The reconstructed network is designed to accurately mimic the topology of the real banking system. Subsequently, a novel framework for measuring aggregate network vulnerability is applied. This framework incorporates factors such as bank size, initial shocks, connectedness, leverage, and asset fire sales to identify financial contagion effects. The findings indicate that the reconstructed network system exhibits a good fit to real-world data in both its linkage structure and weight distribution. Furthermore, the cumulative aggregate vulnerability of the network increases non-linearly with the magnitude of the initial shock and the discount level of asset fire sales. The indirect vulnerability for individual banks, resulting from risk contagion triggered by deleveraging and fire sales, is substantially higher than the direct losses from initial shocks. The risk contribution to systemic vulnerability is concentrated in large state-owned banks and national joint-stock commercial banks. In contrast, the institutions most affected by risk shocks are predominantly small and medium-sized rural and urban commercial banks.

Abstract: This paper features a 1000 simulations of a set of 100 levered companies equity returns in a financial market. The goal was to generate a realistic distribution of company values that follow a Zipf-Mandelbrot power law. The returns should exhibit leverage effects, negative skewness, and feature Black Swan events of correlated down-turns. Realistic positive covariance structures of returns, systematic risk, plus evidence of long-memory properties. The Merton Model and two versions of the Platen Benchmark Asset Pricing Model (BAPM), the original model and the Stochastic Benchmark Process (SBP). The required market attributes were successfuly captured but the models proved to be highly sensitive to the chosen parameters. The BAPM model proved to be more flexible than the Merton Model and the SBP version more readily generated the stipulated financial market characteristics.

Abstract:

This article examines how technological asymmetries—understood as differences in access to advanced digital tools, AI capabilities and IT infrastructure—shape the financial stability and market performance of enterprises of various sizes. The study integrates comparative analyses of 100 industrial joint-stock companies from multiple countries, including technologically advanced large corporations and innovative SMEs, to assess how disparities in digitization and AI implementation influence financial resilience. Using multivariate regression models and index-based financial metrics such as MC, EV, P/E, PEG, P/S, P/B, EV/R and EV/EBITDA, the research identifies relationships between technological advancement, operational efficiency and risk exposure. The findings indicate that companies with higher levels of digitization and AI adoption demonstrate stronger resistance to market disruptions, more effective risk management and more favorable capital structures than SMEs with limited technological resources. However, restricted access to detailed operational data for smaller firms may affect the precision of comparative assessments. The study concludes that investments in digital competences and international cooperation enhance financial stability and support strategic decision-making, while SMEs play an important complementary role by providing outsourcing services that facilitate AI implementation in larger corporations.

Abstract: This study seeks to establish how financial leverage mediates the relationship between corporate governance and firm financial performance of 58 carbon-intensive firms listed on the Johannesburg Stock Exchange for a period 2015-2023. The research employed a two-step system generalized method of moments to address endogeneity issues. The study indicates that leverage negatively impacts firm financial performance; but leverage does not mediate the relationship between corporate governance and firm financial performance in carbon-intensive firms. The results of the study also reveal that board remuneration negatively influences firm financial performance, yet board independence shows insignificant impact on firm performance. These results underscore the need for carbon-intensive companies to reassess their remuneration policies to ensure alignment with short-term financial benefits and long-term sustainability initiatives. The findings also suggest that sustainability projects financed predominantly by debts may negatively impact short- firm financial performance, indicating the importance of balanced capital structure during the decarbonisation process.

Abstract: This study examines how banks navigate the dual strategic imperatives of securing market power and optimizing multidimensional operational efficiency—technical, scale, and allocative efficiency—within emerging and transitional banking systems. Focusing on business model diversification and financial stability, the study also accounts for the conditioning roles of governance quality, institutional complexity, credit risk, and digitalization. Using bank-level data from ASEAN and MENA countries, the analysis applies Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis to assess direct, mediating, and moderating relationships. The results indicate that diversification and financial stability significantly strengthen market power, while their effects on efficiency are largely negative across efficiency dimensions. Governance quality partially mediates the stability–market power relationship, whereas institutional complexity weakens this linkage. Digital transformation maturity and market digitalization condition the diversification–efficiency nexus, with effects varying across efficiency types and regions. Overall, the findings reveal a strategic trade-off between competitive positioning and operational efficiency, emphasizing the importance of governance structures and digital capabilities in shaping bank performance across heterogeneous institutional contexts.

Abstract: 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 [1], De Meyer’s Innovation Ecosystem framework [2], and the Value Chain Theory [3], [4]. This framework enables the exploration of the interaction 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 it was analysed thematically. Based on the interviews with stakeholders, the relative advantage of Latvian FinTechs lies in their flexibility, speed, and trialability; however, the adoption barrier is the complexity of regulation and unevenness in infrastructure and 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.

Abstract: The expansion of internet connectivity and mobile technologies has transformed financial services worldwide, positioning digital banking as a key platform for transactions. In Ethiopia, adoption has accelerated through regulatory reforms and national strategies such as Digital Ethiopia 2025 and the National Financial Inclusion Strategy. Despite these developments, empirical studies remain limited, particularly in urban contexts where usage is rapidly increasing. This study applies the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to examine factors influencing digital banking utilization in Addis Ababa. Using survey data from 405 respondents and Partial Least Squares Structural Equation Modeling (PLS-SEM), the analysis shows that facilitating conditions and price value were the strongest predictors of adoption, followed by performance expectancy and social influence, while effort expectancy was not significant. These findings underscored the importance of infrastructure readiness, affordability, and normative influences in shaping digital banking users. The study contributes to technology adoption literature by contextualizing UTAUT2 within Ethiopia’s financial sector and offers practical insights for policymakers, banks, and technology providers seeking to advance digital financial inclusion.

Abstract: This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely random behavior. By combining price, technical and fundamental inputs, it tests both weak and semi-strong market efficiency. We implement the GRU-D model on a global dataset of stock returns, where daily returns are classified into quartiles. Model performance is assessed using Micro-Average Area Under the Curve (AUC) and Relative Classifier Information (RCI). Robustness checks include sub-sample tests across countries and sectors, an examination of the Covid-19 sub-period, and a price-memory persistence analysis. The results reveal that the GRU-D model achieves a ranking accuracy of approximately 75% when classifying returns, with a statistical significance at the 99.99% confidence level, and exhibits modest but robust deviations from strict market efficiency. These deviations persist for up to 200 trading days. Notably, the findings indicate that the GRU-D model is more robust during the Covid-19 period. These findings are consistent with the Adaptive Markets Hypothesis and underscore the relevance of machine-learning frameworks, particularly those designed for imperfect data environments, for identifying time-varying departures from strict market efficiency in global equity markets.

Abstract: India, a major emerging economy has historically been deeply affected by global economic shocks. Understanding how its key economic factors such as Index of industrial production, wholesale prices, exchange rates and oil prices respond to these events is crucial for the nation's stability. This research aims to analyse India's macroeconomic responses to these three significant global shocks such as Financial crises 2008, recurring oil price shocks and Covid-19 pandemic. Using monthly data from 1993 to 2024, this study employs co-integration tests for long-term linkages and a VECM for short-term dynamics (including IRF and FEVD). Quantile regression uncovers asymmetric crisis effects, whereas ARCH–GARCH models are employed to assess volatility persistence. The findings show long-term equilibrium linkages with significant error-correction. Further, oil price shocks affect inflation and industrial output through exchange rate adjustments. Quantile regression reveals intensified asymmetric effects at distribution extremes whereas Volatility analysis confirms clustering, with structural breaks identified during the Global Financial Crisis and COVID-19. It can be concluded that India's macroeconomic system is externally vulnerable but demonstrates partial resilience. Policy recommendations from this study includes building strategic oil reserves, adopting currency-oil hedging and enhancing overall crisis preparedness so as to achieve macroeconomic stability.