Artificial Intelligence Assisted Forensic Accounting in Fraud Risk Identification

Abstract

This research introduces a novel hybrid methodology that integrates artificial intelligence with forensic accounting principles to create a proactive fraud risk identification
system for financial institutions. Unlike traditional forensic accounting approaches that
rely on post-factum investigation, our framework employs a multi-layered AI architecture that combines quantum-inspired anomaly detection algorithms with federated
learning techniques to identify emerging fraud patterns while preserving data privacy
across institutions. The system utilizes a bio-inspired optimization approach modeled on immune system recognition patterns to detect subtle financial irregularities
that evade conventional rule-based systems. Our methodology represents a paradigm
shift from reactive fraud investigation to predictive risk identification, incorporating
real-time transaction analysis with historical pattern recognition across decentralized
data sources. We developed and tested our framework using a synthetic financial
dataset simulating complex fraud scenarios across multiple banking institutions. Results demonstrate a 47