EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN INTEGRATION FOR FRAUD PREVENTION IN ACCOUNTING INFORMATION SYSTEMS: A SYSTEMATIC REVIEW OF EMPIRICAL EVIDENCE

Abstract

Digital transformation of Accounting Information Systems (AIS) increases operational efficiency while amplifying the risk of accounting fraud. Conventional internal controls are often insufficient to address complex and systematic fraud, driving the adoption of advanced technologies such as Artificial Intelligence (AI) and blockchain. AI supports active fraud detection through anomaly identification and data analytics, whereas blockchain ensures data integrity through transparency and immutability. However, empirical evidence on the effectiveness of their integration in AIS remains limited and fragmented. This study applies a Systematic Literature Review (SLR) to synthesize empirical findings on the effectiveness of AI and blockchain integration for fraud prevention in AIS. Ten empirical journal articles published between 2020 and 2025 were selected from Google Scholar, SINTA, Scopus, and Web of Science. A narrative synthesis was employed to identify patterns, technological roles, integration mechanisms, and implementation factors. The results indicate that AI–blockchain integration provides superior fraud prevention compared to standalone applications. AI primarily enables real-time anomaly detection and fraud analytics, while blockchain functions as a preventive mechanism by ensuring data immutability and reliable audit trails. Key integration mechanisms include anomaly detection, blockchain-based audit trails, smart contracts, and continuous auditing. Implementation effectiveness is strongly influenced by organizational readiness, data quality, human resource competence, IT infrastructure, and regulatory support. This study contributes an empirical synthesis and emphasizes the importance of a socio-technical approach to sustainable fraud prevention in AIS.