Artificial Intelligence and Digital Fraud Mitigation: A Proposed Framework

Abstract

Nigerian financial institutions specifically listed deposit money banks are experiencing maximum number of digital frauds in very recent years. Therefore, this study examines the impact of artificial intelligence to mitigate digital fraud in Nigerian listed deposit money banks. Hence, the moniTARs (monitoring insider trading and outsider) system frame work which includes genetic algorithms and neutral nets for analyzing digital fraud were used to identify digital fraud. The authors summarized prior studies, synthesize contemporary thought and other relevant literature that established negative relationship between variables and proposed future research directions. The finding of this study served as a wakeup call to policy makers and any other relevant authorities to get better policies that can protect myopic behavior of fraudsters. Regulatory authorities (CBN, NDIC, and NCC)
should develop advance machine learning for banks only to detect and prevent patterns of any digital fraud in banking industry. It is suggested that further research should focused on listed insurance firms using any sophisticated algorithms software to prevent myopic behavior of unauthorized people.