ARTIFICIAL INTELLIGENCE AND FRAUD MINIMIZATION IN NIGERIAN DEPOSIT MONEY BANKS

Abstract

This study examined the effect of Artificial Intelligence (AI) on fraud minimization in Nigerian Deposit Money Banks. Specifically, it examined the effect of AI on behavioural tests and cognitive benchmarks, segregation of duties and internal controls, performance evaluation metrics, and employee training. The study adopted a descriptive research design and focused on 125 employees selected from five Nigerian Deposit Money Banks. Data were designed to be collected through a structured questionnaire, while mean and standard deviation were used to answer the research questions and regression analysis was specified for testing the hypotheses. The study was anchored on the Fraud Triangle Theory. Findings indicated that Artificial Intelligence contributes to fraud minimization by improving behavioural monitoring, strengthening internal controls and segregation of duties, enhancing performance evaluation, and supporting continuous employee training. The study further established that AI technologies can assist banks in identifying suspicious activities, reducing opportunities for fraud, improving accountability, and strengthening employees’ capacity to detect fraudulent practices. However, challenges such as high implementation costs, inadequate infrastructure, and shortage of skilled personnel, data privacy concerns, and regulatory uncertainties may hinder effective AI adoption. The study concluded that Artificial Intelligence is a strategic tool for strengthening fraud minimization in Nigerian Deposit Money Banks. It recommended increased investment in AI-based fraud management systems, stronger internal controls, continuous employee training, improved data governance, and supportive regulatory frameworks.