Digital Banking Innovations: An Imperative for Operational Efficiency in Listed Nigerian Deposit Money Banks (Published)
The growing use of digital platforms in banking has greatly changed the way services are provided in Nigeria. There are still differences in how these innovations do, however, perform in terms of operations. The impact of digital banking innovations on operational efficiency of listed Deposit Money Banks (DMBs) in Nigeria (2010-2024) was studied. The study used an ex-post facto type of research design and secondary data was obtained from the annual reports of the 14 listed banks. Digital banking innovations were proxied by Automated Teller Machines (ATM) and Electronic Mobile Banking (EMB) transactions, while operational efficiency was measured by Non-Performing Loans (NPL) and Liquidity Ratio (LR) was used as a control variable. Data was analysed using correlation and panel regression. The results revealed that ATM had positive but insignificant relationship with NPL, whereas electronic mobile banking transactions had negative but insignificant relationship with NPL. Liquidity ratio had a positive and insignificant effect on the operational efficiency. The study finds that digital banking innovations and liquidity management can affect the operational performance, but do not have a significant impact to the extent that the study suggests on the loan performance of listed Nigerian deposit money banks. The study suggests that financial institutions should invest more in their ATM and mobile banking systems, encourage digital banking adoption, strengthen credit risk management, and ensure proper liquidity management to boost efficiency and reduce financial risks
Keywords: Nigerian Banks, Operational Efficiency, automated teller machines (ATM), digital banking innovations, electronic mobile banking (EMB)
Impact of Artificial Based Forensic Accounting on Cyber Fraud in Deposit Money Banks in Nigeria (Published)
This study examines Artificial Intelligence (AI) based forensic accounting and its implications for detecting and preventing high-level cyber frauds in the Nigerian banking sector. Despite the growing adoption of AI-driven systems, Nigerian banks continue to record significant financial losses, raising concerns about the effectiveness of these technologies. Survey researchwas employed, 250 forensic accountants, auditors, IT specialists, and compliance officers across selected banks with respect to adapted questionnaire. Data were analyzed using descriptive statistics, regression analysis, through thematic coding. Findings reveal that AI-based forensic accounting systems exhibit notable flaws, including false positives and negatives, algorithmic bias, lack of transparency, and infrastructural limitations. While respondents acknowledged improvements in fraud detection speed and coverage, statistical results indicated no significant impact of AI on detection and prevention effectiveness. The study concludes that AI, though promising, cannot independently guarantee robust fraud management without human expertise, regulatory support, and infrastructural enhancement. It recommends hybrid approaches integrating AI with forensic accounting judgment, improved data governance, capacity building, and regulatory reforms. The study contributes to forensic accounting and financial technology literature by highlighting the contextual limitations of AI adoption in emerging economies.
Keywords: Artificial Intelligence, Forensic Accounting, Fraud Detection, Fraud Prevention, Nigerian Banks, cyber fraud