Artificial intelligence and fraud detection in University of Calabar Nigeria

Purpose: This study examined the effect of artificial intelligence on fraud detection in the University of Calabar, Nigeria. Specifically, the study investigated the effect of cloud accounting, software automation, data security, regulation, and ethical concerns of artificial intelligence on fraud detection. Methodology: The study adopted a survey research design. The population comprised 830 staff of the University of Calabar, while Taro Yamane formula was used to derive a sample size of 270 respondents. Data was collected through structured questionnaires and analyzed using multiple regression analysis. Results and conclusion: The findings revealed that cloud accounting and data security had negative significant effects on fraud detection, while software automation and regulation had positive effects on fraud detection. Ethical concerns showed a positive but insignificant effect on fraud detection. The study concluded that artificial intelligence significantly influences fraud detection when properly integrated into institutional systems.Implication of findings: Educational institutions should adopt AI-driven fraud detection systems, strengthen data protection policies, and ensure proper regulation and ethical compliance in the use of artificial intelligence technologies.