AI Based Forensic Accounting Tools for Fraud Risk Identification

Abstract

This research introduces a novel, cross-disciplinary methodology for fraud risk identification by integrating principles from forensic accounting, behavioral economics, and artificial
intelligence. Departing from conventional rule-based or purely statistical anomaly detection systems, we propose the Cognitive Anomaly and Pattern Synthesis (CAPS) framework.
CAPS employs a hybrid architecture that combines a symbolic reasoning layer, modeled on
forensic accounting heuristics and known fraud schemes, with a connectionist deep learning
component trained to identify subtle, non-linear patterns indicative of emergent fraudulent behaviors not previously cataloged. A key innovation is the synthesis of ’negative
exemplars’—simulated fraudulent financial scenarios generated via adversarial neural networks—to augment training data and stress-test detection models, thereby addressing the
critical challenge of limited real-world fraud data. Furthermore, the system incorporates a
temporal narrative reconstruction module that sequences discrete anomalies into plausible
’fraud stories,’ providing auditors with interpretable causal hypotheses rather than mere
alerts. Our evaluation on a synthesized multi-entity transaction dataset, incorporating elements from public financial statements and simulated malfeasance, demonstrates that the
CAPS framework achieves a 34