Enhancing fraud prevention with artificial intelligence in accounting systems: A case study of e-fishery - Suyatna, Nano (Jurnal Mantik, 2025)
Fraud in accounting records, particularly income inflation, poses a significant risk to companies, affecting credibility, investment decisions, and legal compliance. Despite using the double-entry system, financial statement manipulation can still occur, creating an illusion of higher profitability. This study explores how Artificial Intelligence (AI) in Accounting Information Systems can more effectively prevent income inflation and detect fraud than traditional methods. A case study of e-Fishery highlights AI’s role in identifying fraud through anomaly detection and automated general journal verification. AI-based audits, such as those using Isolation Forest, significantly improve efficiency by automating repetitive tasks and enabling real-time data analysis, reducing the time and resources required for audits. The research results indicate that 68% of respondents preferred the automated audit approach. The Isolation Forest algorithm resulted in a detection accuracy of 26%, while Autoencoder improved the accuracy to 33.6%. These findings demonstrate that AI in Accounting Information Systems enhances fraud prevention, improves financial reporting accuracy, and addresses challenges traditional methods fail to identify