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

Abstract

This research investigates the application of machine learning techniques, speci}cally deep neural networks, in detecting }nancial statement
manipulation within forensic accounting contexts. We developed and
tested multiple neural network architectures on a comprehensive dataset
of 15,000 corporate }nancial statements spanning 2000-2003, including
both legitimate and manipulated cases identi}ed through regulatory actions. Our methodology employed feature engineering of }nancial 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, signi}cantly outperforming traditional statistical methods and human expert analysis. The model successfully identi}ed 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 }eld 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.