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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia

Author

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  • Cheng-Wen Lee

    (Department of International Business, Chung Yuan Christian University, Taoyuan 320314, Taiwan)

  • Mao-Wen Fu

    (Ph.D. Program in Business, College of Business, Chung Yuan Christian University, Taoyuan 320314, Taiwan)

  • Chin-Chuan Wang

    (Ph.D. Program in Business, College of Business, Chung Yuan Christian University, Taoyuan 320314, Taiwan)

  • Muh. Irfandy Azis

    (Department of Accounting, Universitas Borneo Tarakan, Tarakan 77123, Indonesia)

Abstract

The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. Model performance was evaluated using key metrics, including precision, recall, accuracy, and F1-Score. The analysis also identified significant indicators of fraud, such as Accounts Receivable Turnover, Days Outstanding Accounts Receivable, Days Payables Outstanding, Logarithm of Gross Profit, Gross Profit Margin, Inventory to Sales Ratio, and Total Asset Turnover. Among the models, Random Forest emerged as the most effective algorithm, consistently outperforming others on both training and testing datasets. Logistic Regression and SVM demonstrated strong reliability, whereas KNN and Decision Tree faced overfitting challenges, limiting their practical application. These findings emphasize the critical need for enhanced fraud detection frameworks, leveraging machine learning algorithms like Random Forest to identify fraud patterns effectively. The study highlights the importance of strengthening internal controls, implementing targeted fraud detection measures, and promoting regulatory improvements to enhance transparency and financial accountability.

Suggested Citation

  • Cheng-Wen Lee & Mao-Wen Fu & Chin-Chuan Wang & Muh. Irfandy Azis, 2025. "Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia," Mathematics, MDPI, vol. 13(4), pages 1-35, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:4:p:600-:d:1589542
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    References listed on IDEAS

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    1. NIAMH M. BRENNAN & MARY McGRATH, 2007. "Financial Statement Fraud: Some Lessons from US and European Case Studies," Australian Accounting Review, CPA Australia, vol. 17(42), pages 49-61, July.
    2. Messod D. Beneish, 1999. "The Detection of Earnings Manipulation," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(5), pages 24-36, September.
    3. M. Brownbridge & C. Kirkpatrick, 2000. "Financial Regulation in Developing Countries," Journal of Development Studies, Taylor & Francis Journals, vol. 37(1), pages 1-24, October.
    4. Peter Tickner & Mark Button, 2021. "Deconstructing the origins of Cressey’s Fraud Triangle," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 28(3), pages 722-731, June.
    5. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
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