Posted: Dec 04, 2023
This research presents a novel computational framework for evaluating the effectiveness of forensic accounting techniques in detecting corporate misappropriation, bridging the gap between traditional accounting practices and advanced computational analytics. Unlike previous studies that focus primarily on individual techniques or post-facto analysis, our approach integrates multiple forensic methodologies within a unified computational environment to assess their synergistic effectiveness. We developed a simulated corporate environment generating synthetic financial data with embedded misappropriation patterns, allowing for controlled testing of detection techniques. The methodology incorporates three innovative components: a multi-agent system simulating corporate behavior, a quantum-inspired pattern recognition algorithm adapted for financial anomaly detection, and a bio-inspired optimization framework for technique selection. Our results demonstrate that traditional forensic accounting methods achieve only 67
Downloads: 52
Abstract Views: 737
Rank: 28780