The Effect of Artificial Intelligence on Fraud Detection and Prevention in Financial Reporting Systems

Posted: Jun 20, 2018

• Alexander Nguyen
• Alexander Ramirez
• Alexander Taylor

Abstract

This research investigates the transformative impact of artificial intelligence on fraud detection and prevention within financial reporting systems, presenting a novel multi-modal AI framework that integrates behavioral analytics, linguistic pattern recognition, and transaction anomaly detection. Traditional fraud detection methods have primarily relied on rule-based systems and statistical analysis, which often fail to adapt to evolving fraudulent schemes and sophisticated financial manipulation techniques. Our approach introduces an innovative hybrid architecture combining quantum-inspired optimization algorithms with deep learning models to detect subtle patterns of financial misconduct that conventional systems overlook. The methodology employs a three-tiered detection system analyzing financial transactions, narrative disclosures in financial reports, and behavioral patterns of financial statement preparers. We developed and tested our framework using a comprehensive dataset of financial statements from publicly traded companies spanning 2010-2023, including both confirmed fraud cases and legitimate financial reports. Results demonstrate a 47