ADVANCED INTERNATIONAL JOURNAL OF BANKING, ACCOUNTING AND FINANCE (AIJBAF)

THE IMPACT OF FINANCIAL LITERACY ON ROBO-ADVISOR ADOPTION DURING ECONOMIC CRISIS: EVIDENCE FROM MALAYSIA

Najihah Abd Razak — 2026-09-09

Despite the potential benefits of robo-advisors, their adoption in Malaysia lags other East Asian developing economies, particularly during the recent economic crisis. The connection between the dimensions of financial literacy and the use of robo-advisors has not been adequately studied in the context of AI-assisted services in the past. This gap highlights the need to explore underlying behavioral and perceptual factors influencing the adoption of robo-advisors. This study builds upon the Technology Acceptance Model (TAM) by adding the financial literacy dimensions as external antecedents of perceived usefulness and behavioural intention to adopt robo-advisors. This addition expands the traditional TAM by showing that the adoption of robo-advisors is not only influenced by the perceptions of the users towards the technology itself, but also by their financial capabilities and orientations. A survey questionnaire was used to collect data on 380 Malaysian retail investors, which was analyzed by Partial Least Squares Structural Equation Modeling (PLS-SEM). Financial knowledge (β=0.613, p<0.05) and financial attitude (β=0.292, p<0.05) were found to have significant positive effects on perceived usefulness, and perceived usefulness exhibited a significant positive effect on intention to use (β=0.671, p<0.05), confirming its central role as a determinant of behavioral intention in this model. The model was observed to have a moderate predictive ability of behavioral intention to use by 53.2%. The results add to the existing literature on the adoption of financial technologies in emerging markets and have a number of practical implications. The providers must make conscious efforts to demonstrate the practical worth of their services by case studies, live demonstrations and interactive simulations that display the performance of robo-advisors in volatile markets. To policymakers and regulators, the findings highlight the importance of focusing on how individuals can use their skills with technology to deal with risk and improve financial decision-making in crisis situations.

SUSTAINING THE TAKAFUL INDUSTRY IN MALAYSIA: CRITICAL FACTORS AND STRATEGIES

Muhammad Zarunnaim Wahab — 2026-09-09

This paper aims to outline the future direction of Malaysia's takaful industry by highlighting several crucial aspects that takaful operators should focus on to ensure long-term sustainability. To accurately address the research objectives, a qualitative research methodology was employed. Primary data was collected through in-depth interviews with four informants who possessed expertise in various fields related to takaful. The analysis identified six critical areas that require attention for the sustenance of the Malaysian takaful industry: increasing the number of retakaful companies, introducing Sharia parameters, establishing a clear insolvency framework, ensuring transparency in the legal and operational framework, enhancing the knowledge and trustworthiness of agents, and conducting comprehensive research. This study has several limitations. Firstly, the focus on the perspectives of takaful operators (TOs) in Malaysia may limit the generalizability of the findings to TOs in other countries. Secondly, the small number of informants interviewed may not fully capture the diversity of opinions and experiences in the industry. Future research could address these limitations by expanding the scope to include multiple countries and increasing the sample size of informants. The findings provide valuable insights for takaful operators, guiding them in developing future business targets and directions. These results are significant for the industry, offering strategies to increase market share and deliver better quality products and services to customers.

MONETARY DEPTH AND ECONOMIC ACTIVITY AS DRIVERS OF PRIVATE CREDIT IN UGANDA

David Namanya — 2026-09-09

The main objective of this study was to examine the short run relationship between monetary depth, economic activity and annual changes in private sector bank credit in Uganda from 1994 to 2025, with particular emphasis on changes in broad money, real gross domestic product growth, inflation and lagged movements in private sector credit. To achieve this objective, annual data were obtained from the World Development Indicators and analysed using an ordinary least squares regression model with Newey West heteroscedasticity and autocorrelation consistent standard errors, while private sector credit and broad money were measured as annual changes in their respective ratios to gross domestic product. The results indicated that the model explained 81.4 per cent of the variation in annual changes in private sector credit; furthermore, changes in broad money and real gross domestic product growth had positive and statistically significant relationships with private sector credit. Although inflation had a positive relationship and the lagged change in private sector credit had a negative relationship, neither variable was statistically significant. Overall, the findings demonstrate that Uganda’s private sector credit dynamics are influenced by both banking system liquidity and prevailing economic activity; however, the less than proportional response of credit to broad money suggests that liquidity alone is insufficient to stimulate lending. The study recommends strengthening credit information systems, movable collateral registration, transparent loan pricing, financial sector competition and risk-based lending technologies, while recognising that the reported relationships represent short run associations rather than definitive causal effects.

COMPARATIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE METHODS AND TRADITIONAL CREDIT RISK MODELS: AN EXPLORATORY QUALITATIVE REVIEW

Sadia Asif — 2026-09-21

Artificial intelligence (AI) and machine learning (ML) methods are revolutionizing the credit risk modelling. Traditional statistical methods which have been around for a long time are replaced with AI-based models. Although AI models provide superior predictive performance, their adoption in regulated financial sector is seen with caution. Financial sector regulatory requirements regarding interpretability, stability, fairness, and governance of credit risk models are one of the big challenges to AI & ML adoption. This study is based on an exploratory qualitative review to perform a comparative analysis of AI models and traditional credit risk models. This study examines relevant academic literature, publications, and industry reports. Five key points have been identified as a result of this study: (1) AI models are better than traditional models in terms of performance; (2) explainability and auditability is still the biggest challenge with AI adoption; (3) traditional models are more reliable yet they may not be efficient; (4) regulatory requirements impact model selection; and (5) hybrid modelling approaches are a promising middle ground. The article suggests a multidimensional theoretical framework for assessing credit risk models and emphasizes prospective research avenues in hybrid modelling, fairness-aware AI, and real-time risk monitoring.

PRIVATE SECTOR BANK CREDIT, FINANCIAL DEEPENING AND ECONOMIC GROWTH IN UGANDA: AUTOREGRESSIVE DISTRIBUTED LAG

David Namanya — 2026-09-21

This study evaluated the short run relationships among private sector bank credit, monetary depth, inflation and real output in Uganda from 1994 to 2025 and tested whether the variables shared a stable long run relationship. Annual data from the World Development Indicators were analysed using descriptive statistics, Augmented Dickey–Fuller (ADF) unit root tests and autoregressive distributed lag (ARDL) bounds testing. The Akaike information criterion selected an ARDL model with lag order (2,2,2,2). The bounds test F statistic of 1.684 was below the 5 per cent lower and upper critical values of 4.01 and 5.07, so no stable long run cointegrating relationship was established. In the short run, private sector credit was positively associated with real output in the current year (β = 0.0180, p = 0.005) and after one year (β = 0.0140, p = 0.044). In these results, β denotes the estimated regression coefficient and p denotes the probability value used to assess statistical significance. Broad money had negative conditional associations in the current year (β = −0.0090, p = 0.024) and after one year (β = −0.0075, p = 0.035). Current inflation was insignificant (β = −0.0010, p = 0.458), while lagged inflation was negative and significant (β = −0.0019, p =0.045). The findings indicate that the relationship between finance and output depends not only on monetary depth but also on the extent to which banks channel funds to private borrowers. Uganda should thus strengthen credit information, collateral systems and access to affordable long-term finance, while directing greater attention to productive sector lending.