(untitled) (gjstudies, 2025)
This research introduces a novel hybrid methodology that integrates artificial intelligence with forensic accounting principles to create a proactive fraud risk identificationsystem for financial institutions. Unlike traditional forensic accounting approaches thatrely on post-factum investigation, our framework employs a multi-layered AI architecture that combines quantum-inspired anomaly detection algorithms with federatedlearning techniques to identify emerging fraud patterns while preserving data privacyacross institutions. The system utilizes a bio-inspired optimization approach modeled on immune system recognition patterns to detect subtle financial irregularitiesthat evade conventional rule-based systems. Our methodology represents a paradigmshift from reactive fraud investigation to predictive risk identification, incorporatingreal-time transaction analysis with historical pattern recognition across decentralizeddata sources. We developed and tested our framework using a synthetic financialdataset simulating complex fraud scenarios across multiple banking institutions. Results demonstrate a 47