Digital financial inclusion is a fundamental component in alleviating poverty and enhancing prosperity. A comprehensive financial system lays the foundation for a basis of robust and resilient households, enhancing the welfare of low-income groups. This research examines the determinants of digital financial inclusion among low-income families in Malaysia, specifically focusing on digital literacy, financial literacy, trust, infrastructure and financial service providers. Total of one thousand one hundred seventy-one respondents with income below the national poverty line were surveyed. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) and the Multigroup Analysis (MGA) techniques were employed. This study separated the total sample into two groups, Peninsular Malaysia and East Malaysia. The results demonstrate that trust, digital literacy and infrastructure are positively and significantly related to digital financial inclusion for all the groups. The role of financial service providers is only supported for the complete sample set and Peninsular Malaysia. Surprisingly, this study found no association between financial literacy and digital financial inclusion. In addition, the MGA results further evidence significant differences between Peninsular Malaysia and East Malaysia in achieving digital financial inclusion. Peninsular Malaysia shows stronger digital literacy and infrastructure, while East Malaysia demonstrates more potents effects of financial service providers and trust in boosting digital financial inclusion. These findings provide practitioners valuable insights into fostering a digitally inclusive financial society. This research includes strengthening consumer protection regulations on digital financial services to address cyber threats, implementing digital literacy campaigns to enhance awareness, and ensuring affordable and reliable internet access. These policy recommendations highlight the importance of formulating and designing tailored strategies to enhance digital financial inclusion among low-income households in Malaysia.

Integrating Artificial Intelligence (AI) into investment finance has been transformative; however, its dynamic evolution in asset management remains underexplored. This study aims to comprehend and investigate the presence of AI in Malaysian asset management, its evolution in relation to current traditional practices, and its performance. A mixed-methods approach was employed. Qualitative text analysis was conducted on 702 fund reports and official AMC documents to identify the presence of AI technologies and their role in transforming traditional practices. Quantitative methods were utilised to evaluate performance: user ratings of robo-advisor applications were analysed to measure adoption and satisfaction, while Welch's t-test compared the annual returns of AI-driven and human-managed equity funds to assess performance. This study identified two prominent AI in asset management: robo-advisors and AI-driven funds. Robo-advisors automate investor profiling and fund recommendations, whereas AI-driven funds use algorithms for autonomous trading decisions. AI adoption varies among asset management companies (AMCs), with differing levels of integration. This transformation has supplanted traditional unit trust consultants (UTCs) with robo-advisors and human fund managers with AI-driven funds. Preliminary findings indicate high user satisfaction with robo-advisors because of their efficacy and convenience. AI-driven funds yield annual returns comparable to those of human-managed funds in the equity category, demonstrating their proficiency in managing complex investment strategies. These findings illuminate AI's transformative potential in asset management, suggesting it could replace traditional roles and achieve competitive performance. This study lays the groundwork for future research on AI's long-term impact and scalability in the industry, enriching the understanding of AI's evolving role in finance.

This study explores the impact of economic policy uncertainty on herd behaviour in the Chinese stock market. As economic policy uncertainty increases, market information becomes highly chaotic and complex, making reliable information scarce and challenging for investors to make independent decisions. Particularly in the Chinese stock market, where retail investors dominate and generally lack professional financial knowledge and deep market analysis skills, these investors are more likely to mimic the behaviours of other market participants. Using monthly data from January 2011 to December 2023, and employing panel regression for empirical analysis, this research aims to explore the specific effects and mechanisms of economic policy uncertainty on herd behaviour, addressing a gap in the existing literature regarding how economic policy uncertainty directly influences investor behaviour in terms of manner and extent. The study's findings indicate that economic policy uncertainty has a significant and varied impact on herding behaviour across different market segments. Specifically, economic policy uncertainty significantly promotes herding behaviour in the Science and Technology Innovation Board, while it inhibits herding behaviour in the Main Board. Economic policy uncertainty also inhibits herding behaviour in the ChiNext, but not as significantly as in the Main Board. This diversity in impact highlights the complex nature of economic policy uncertainty's influence on herd behaviour.

In this contemporary era, mobile banking services enable customers to organise and accomplish cashless financial transactions using mobile devices. However, the general state of banks' cybersecurity systems significantly impacts the usage of banking services offered through mobile networks among customers. The analysis was performed to assess how customers perceive the cybersecurity systems of banks and their association with mobile banking usage. By adopting a cross-sectional research design under the guidance of the Unified Theory of Acceptance and Use of Technology (UTAUT), the Partial least squares structural equation modelling (PLS-SEM) analysis reveals that banks' cybersecurity systems' performance expectancy had a significant positive impact on the use of mobile banking services. Further, banks' cybersecurity systems' effort expectancy, significant others' comments and facilitating conditions significantly and positively influence bank customers to use mobile banking. Aligning with these findings, banks and financial institutions should prioritise and strengthen cybersecurity systems and simplify mobile banking processes for enhanced mobile banking adoption and usage among bank customers.

This study discovers the influence of the firm’s characteristics (i.e. firm size, firm leverage, aggressiveness of sales, board of directors’ size and diversity) on the lapse rate of life insurance policies and family takaful certificates in Malaysia. The panel data includes 25 insurance operators consisting of 14 conventional insurers and 11 takaful operators available between 2014 and 2022.The cross-sectional Ordinary Least Square (OLS) estimates show that firm size has a negative influence on lapse rates of life insurance (2019-2021), while a positive sign for firm leverage (2017, 2020 and 2021). The aggressiveness of sales shows the inconsistent influence on the lapse rate of life insurance in 2017 (positive) and in 2021 (negative). The firm leverage negatively influences on the lapse rate of family takaful for 2017 and 2018. The panel data estimates show that firm leverage and diversity of the firm’s board of directors significantly impact the life insurance lapse rate while only firm diversity significantly impacts family takaful lapse rates. This study offers policy implications for the insurance industry in Malaysia, especially by the Bank Negara Malaysia (BNM).

This paper aims to assess the common characteristics of dividend payers and non-payers in both China and the US, as well as the stability of dividend policies in each respective nation. The study addresses issues related to catering theory, signal theory, smoothing assumptions, and Fama-French hypotheses. Our analysis covers 509 non-financial firms during the period 2006-2016, comprising 419 firms from the US and 90 firms from China. The multinomial regression reveals that past dividend payouts are critical in explaining dividend policy in both the US and China. Firms that have consistently paid higher dividends in the preceding year are more likely to opt for dividend increases rather than omitting dividends, thus aligning with the dividend smoothing hypothesis. Additionally, firm size and stock market performance were found to be significantly influencing dividend payouts across different categories in both the US and China. However, US firms primarily drove dividend payouts through profitability and the dividend premium, while firm size and leverage heavily influenced Chinese dividends. Interestingly, Chinese firms appear to practice a less sticky dividend policy than US firms, implying that the role played by dividend policy in signalling and catering theories in China is less significant than in the US.

This study used quarterly data for the period of 2014Q1–2021Q2 to investigate the macro-econometric implications of capital flows to Nigeria in the face of security challenges. The ARDL bounds test technique identified the long-run relationships between macroeconomic dynamics, insecurity, and total capital inflows to Nigeria, while the error correction mechanism (ECM) identified the short-run relationships. The Toda-Yamamoto test was used to determine whether the model variables were causally related or not. The findings pointed to a short-term negative relationship between insecurity, exchange rates, lending rates, and inflows of capital, while a positive relationship was found between industrial production capacity, the consumer price index, and the total inflows of capital to Nigeria, with insecurity, exchange rates, the consumer price index, and lending rates being the most significant variables. In the long run, insecurity, lending rate, and consumer price index had no significant impact on inward capital inflows, while exchange rate and industrial production capacity exerted significant impacts on capital inflows. The lending rate had a negative impact on overall inflows of capital, whereas the exchange rate, industrial production capacity, and consumer price index had positive impacts. The exchange rate and industrial production capacity were the most important variables that affected capital inflows. Based on the ECM, it was realized that aggregate inward capital flows were stabilized by a factor of roughly 47.2% per quarter in order to reach long-run equilibrium. The Toda-Yamamoto causality tests indicated that the interactions between macroeconomic variables and insecurity strongly influenced capital flows to Nigeria. The overall findings suggested that promoting macroeconomic stability and combating insecurity could improve the investment climate, encouraging foreign capital flows into Nigeria.

This study proposes nine multivariate intraday models using various realized variation measures with the aim to improve volatility forecasting in the Islamic stock market in Malaysia using a dataset from 1st April 2008 to 31st March 2018. The findings show that considering independently the jump-robust realized volatility, additional daily jump realized volatility, and continuous and discontinuous jump sample path variations improved the in-sample predictive regressions compared to using the standard realized volatility. For the out-of-sample volatility forecasts evaluation, it is observed that the volatility models that disentangled the realized volatility into its continuous and discontinuous jump components have outperformed the rest of the proposed models. This is because both the continuous and discontinuous variation of returns exhibit distinctive substantial information in yielding the final volatility dynamic and thus should be modeled disjointedly. However, the empirical results suggest that the simple autoregressive specification using the standard realized volatility is often performing better or as well as the new extension models. Lastly, this study may provide useful insight in portfolio management, risk assessment, and asset pricing, particularly in the Shariah-compliant equities.

The basic concept of credit scoring is to assess an individual`s payment ability as well as the specific individual`s credit default risk, hence determining an individual`s creditworthiness. Based on the credit score, financial institutions, insurance companies, telecommunication companies and other businesses decide whether consumers are eligible for a mortgage, credit card, auto loan, and other credit products. However, in many countries, potential tenants and insurance applicants also use credit scores extensively for screening. Accordingly, Credit Bureaus (CB) or Consumer Reporting Agencies (CRA) exert an essential gatekeeper function for important economic areas of consumers’ everyday life. However, when examining CBs globally, there are considerable differences in the use of data to calculate credit scores. Interestingly, the influence of CBs on credit rating receives little to no attention in academic research. This is particularly evident in the absence of a framework for classifying Credit Bureaus. Therefore, 24 traditional and non-traditional Credit Bureaus operating in 17 different countries are analyzed. First, the study identifies the different data types underlying credit reports and credit scores. Second, CBs are classified and clustered according to the type of information used for credit scoring. Furthermore, promising areas of research, in particular the ethical conflict between data protection and economic participation are highlighted.