Machine Learning Applications in Forensic Accounting: Detecting Financial Statement Manipulation Using Neural Networks (gjstudies, 2020)
This research investigates the application of machine learning techniques, speci}cally deep neural networks, in detecting }nancial statementmanipulation within forensic accounting contexts. We developed andtested multiple neural network architectures on a comprehensive datasetof 15,000 corporate }nancial statements spanning 2000-2003, includingboth 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 patternrecognition. The results demonstrate that our optimized convolutionalneural 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 patternsin revenue recognition timing, expense capitalization, and related-partytransactions that are typically challenging for manual detection. Thisresearch contributes to the growing }eld of computational finance by providing a robust framework for automated financial fraud detection thatcan assist auditors, regulators, and investors in early identification of accounting irregularities.