Machine Learning Applications in Forensic Accounting: Detecting Financial Statement Manipulation Through Neural Network Analysis

Posted: Mar 11, 2024

• Wei Zhang
• Isabella Rossi
• Kenji Tanaka
• Fatima Al-Mansoori

Abstract

This research investigates the application of machine learning techniques, specifically neural networks, in detecting financial statement manipulation within forensic accounting contexts. The study develops and validates a multi-layer perceptron model trained on comprehensive financial datasets from publicly traded companies spanning 2000-2003. Our methodology incorporates 27 financial ratios and operational metrics as input features, with labeled instances of confirmed financial manipulation cases. The model achieved 94.3% accuracy in detecting manipulation patterns, significantly outperforming traditional statistical methods. Results demonstrate that neural networks can effectively identify subtle patterns indicative of earnings management and fraudulent reporting. The research contributes to both accounting practice and financial technology by providing a robust automated detection framework that enhances audit efficiency and early warning capabilities for financial regulators.