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

Posted: Jul 03, 2024

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

Abstract

This research investigates the application of machine learning techniques, specifically neural networks, in detecting financial statement fraud within forensic accounting. The study develops a comprehensive fraud detection model using financial ratios, transactional patterns, and corporate governance indicators from 1,200 publicly traded companies spanning 2010-2023. Our methodology employs a multi-layer perceptron neural network architecture trained on validated fraud cases identified by regulatory authorities. The model achieves 94.2% accuracy in fraud classification, significantly outperforming traditional statistical methods. Key predictive variables include abnormal accruals, related-party transaction frequency, and board independence metrics. The research demonstrates that machine learning approaches can substantially enhance fraud detection capabilities in accounting practice, providing auditors and regulators with more effective tools for financial crime prevention.