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Vol. 6 No. 1 (2025)
2025 Edition

Articles

Machine Learning Applications in Financial Statement Fraud Detection: A Comparative Analysis

Published 2025-10-14

  • Sheng Xu
    • ,
  • Yuyu Zhou

Sheng Xu
Computer Architecture, University of Southern California, LA, USA

Yuyu Zhou
Information Analytics, University of New Hampshire, NH, USA

Abstract

Financial statement fraud poses significant threats to market integrity and investor confidence worldwide. This research investigates the application of multiple machine learning algorithms in detecting fraudulent patterns within corporate financial reporting. Through systematic comparison of classification techniques including Random Forest, Support Vector Machine, Logistic Regression, and Gradient Boosting methods, this study evaluates detection performance across diverse financial datasets. The experimental framework encompasses 28 financial ratio features extracted from publicly traded companies over a five-year period. Performance metrics including accuracy, precision, recall, F1-score, and AUC-ROC values are employed to assess algorithm effectiveness. Results demonstrate that ensemble methods achieve superior detection capabilities, with Random Forest attaining 94.7% accuracy and 0.963 AUC score. The comparative analysis reveals critical insights into algorithm selection strategies based on dataset characteristics and computational requirements. Findings provide practical guidance for auditors and regulatory bodies in implementing automated fraud detection systems. This research contributes to the growing intersection of artificial intelligence and financial compliance through empirical validation of machine learning techniques in real-world fraud detection scenarios.

Keywords

Financial fraud detection, , Machine learning algorithms, Financial statement analysis, Fraud classification

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