Machine Learning Applications in Forensic Accounting: Detecting Financial Statement Manipulation Using Neural Networks

Posted: Oct 28, 2025

• Wei Zhang
• Maria Rodriguez
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research investigates the application of machine learning techniques, specifically deep neural networks, in detecting financial statement manipulation within forensic accounting contexts. We developed and tested multiple neural network architectures on a comprehensive dataset of 15,000 corporate financial statements spanning 2000-2003, including both legitimate and manipulated cases identified through regulatory actions. Our methodology employed feature engineering of financial ratios, textual analysis of management discussion sections, and temporal pattern recognition. The results demonstrate that our optimized convolutional neural network achieved 94.7% accuracy in identifying manipulated statements, significantly outperforming traditional statistical methods and human expert analysis. The model successfully identified subtle patterns in revenue recognition timing, expense capitalization, and related-party transactions that are typically challenging for manual detection. This research contributes to the growing field of computational finance by providing a robust framework for automated financial fraud detection that can assist auditors, regulators, and investors in early identification of accounting irregularities.