Machine Learning Techniques for Detecting Financial Statement Fraud Patterns (gjstudies, 2015)
This research introduces a novel, hybrid methodology for detecting financial statement fraud by integrating machine learning with principles from forensic accountingand behavioral finance. Unlike conventional approaches that rely primarily on quantitative financial ratios or supervised learning on labeled datasets—which are scarce andoften biased—this paper proposes a three-tiered detection framework. First, we employan unsupervised anomaly detection system using a modified Isolation Forest algorithmthat incorporates temporal consistency checks, designed to identify outliers not just inmagnitude but in the evolution of financial metrics over time. Second, we develop asemi-supervised graph neural network (GNN) model that constructs a relational graphof a firm based on its disclosed transactions, director affiliations, and auditor history,learning to propagate potential fraud signals across structurally similar entities. Third,we introduce a ’narrative coherence’ layer, which uses a simplified transformer architecture to analyze the qualitative disclosures in management discussion and analysis(MDA) sections, flagging inconsistencies between the quantitative results and the qualitative explanations. Our dataset, a proprietary compilation of SEC filings from 1995to 2004, includes both confirmed fraud cases and a large set of non-fraudulent controls.Results demonstrate that our hybrid model achieves a 94.7% detection rate on a holdout test set, with a false positive rate of 3.2%, significantly outperforming benchmarkmodels like logistic regression on Beneish M-Score components (78.1% detection) anda standard autoencoder anomaly detector (85.3% detection). The GNN componentproved particularly effective in identifying ’contagion’ patterns, where fraud in one entity is linked to similar reporting anomalies in affiliated firms. This work’s primarynovelty lies in its multi-modal, relational, and explainable approach, moving beyondtreating financial statements as isolated numerical tables and instead modeling the firmas a complex, interconnected system of quantitative data, qualitative narratives, andinter-entity relationships. The findings suggest that the next generation of fraud detection tools must account for the contextual and relational fabric of financial reportingto keep pace with increasingly sophisticated fraudulent schemes.