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Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud

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  • Adrian Gepp
  • Kuldeep Kumar
  • Sukanto Bhattacharya

Abstract

This study enables practitioners and researchers to make an informed choice for a financial statement fraud detection model, rather than defaulting to popular, yet dated, models. Using a specifically devised performance criterion, our newly configured ensemble outperforms 31 others in the most comprehensive comparison to date spanning parametric, non‐parametric, big data and ensemble techniques. We use a large set of input variables and holdout data relative to prior studies. We find empirical support for financial and non‐financial variables covering the three Fraud Triangle factors. New findings include fraud risk being reduced with more debt, likely from increased monitoring by creditors.

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  • Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2021. "Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4601-4638, September.
    • Handle: RePEc:bla:acctfi:v:61:y:2021:i:3:p:4601-4638
    DOI: 10.1111/acfi.12742
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    Cited by:

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    3. Esraa Faisal Malik & Khai Wah Khaw & Bahari Belaton & Wai Peng Wong & XinYing Chew, 2022. "Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture," Mathematics, MDPI, vol. 10(9), pages 1-16, April.

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