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Taking the hunch out of the crunch: A framework to improve variable selection in models to detect financial statement fraud

Author

Listed:
  • Adrian Gepp
  • Kuldeep Kumar
  • Sukanto Bhattacharya

Abstract

Financial statement fraud is a costly problem for society. Detection models can help, but a framework to guide variable selection for such models is lacking. A novel Fraud Detection Triangle (FDT) framework is proposed specifically for this purpose. Extending the well‐known Fraud Triangle, the FDT framework can facilitate improved detection models. Using Benford's law, we demonstrate the posited framework's utility in aiding variable selection via the element of surprise evoked by suspicious information latent in the data. We call for more research into variables that measure rationalisations for fraud and suspicious phenomena arising as unintended consequences of financial statement fraud.

Suggested Citation

  • Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2024. "Taking the hunch out of the crunch: A framework to improve variable selection in models to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(2), pages 1569-1588, June.
    • Handle: RePEc:bla:acctfi:v:64:y:2024:i:2:p:1569-1588
    DOI: 10.1111/acfi.13192
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    References listed on IDEAS

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    Cited by:

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    2. Feng, Lingbing & Zheng, Yuhao & Wang, Xinyi & Guo, Chuan & Xue, Rui, 2025. "Global stock market forecasting: Insights from series and parallel combination of machine learning models," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).

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