Fraud remains a significant challenge for the global financial sector, with substantial financial losses and eroded trust in banking systems. In Nigeria, Deposit Money Banks (DMBs) have been severely impacted by rising fraud activities, including identity theft, account takeovers, and cybercrimes. With the expansion of digital banking and online services, fraud has become more sophisticated, prompting the need for advanced fraud prevention methods. This study explores the effect of Artificial Intelligence (AI) on fraud prevention in Listed DMBs. This study adopts a survey research design, which is considered appropriate due to the nature of the research objectives and the methodology employed. A survey research design is particularly well-suited for collecting data from a large group of individuals to gain insights into their perceptions, experiences, and attitudes on a particular topic. The population for this study consists 100 Hundred of the staff and management of the 14 listed deposit money banks operating in Nigeria as of December 31, 2024. The regression analysis shows that ADS accounts for approximately 29.8% of the variation in FFP, indicating a strong and meaningful relationship between the two. As ADS improves, so does the effectiveness of fraud prevention, with each increase in ADS leading to a notable improvement in FFP. However, the model also suggests that there are other important factors contributing to FFP, as evidenced by the 70% of variability not explained by ADS alone. While ADS proves to be a valuable predictor, it is clear that a comprehensive fraud prevention strategy should integrate ADS with other methods, such as human oversight, user behavior analytics, and rules-based systems, to ensure a more holistic and effective approach to combating financial fraud. The study recommends that Integrate BAS with Other Fraud Detection Techniques: Given the significant yet partial contribution of BAS to FFP, it is recommended that organizations combine BAS with additional fraud prevention systems and Focus on Continual Improvement and Calibration of ADS: Since ADS has proven to be a strong predictor of FFP, organizations should invest in the continual improvement and fine-tuning of their anomaly detection systems.