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

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Adrian Gepp , Kuldeep Kumar , Sukanto Bhattacharya ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1111/acfi.13192

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.

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Financial statement Crunch Statement (logic) Variable (mathematics) Selection (genetic algorithm) Computer science Surprise Problem statement Benford's law Accounting Economics Machine learning Psychology Mathematics Management science Statistics Law Political science Physical therapy Medicine Mathematical analysis Audit Social psychology

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Type: article
Language: en
Landing Page: https://doi.org/10.1111/acfi.13192
PDF: https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/acfi.13192
OA Status: hybrid
Cited By: 5
References: 59
Related Works: 10
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DOI: https://doi.org/10.1111/acfi.13192

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

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Type: article

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Language: en

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Publication year: 2023

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Publication date: 2023-10-27

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Authors: Adrian Gepp, Kuldeep Kumar, Sukanto Bhattacharya

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Landing page: https://doi.org/10.1111/acfi.13192

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Concepts: Financial statement, Crunch, Statement (logic), Variable (mathematics), Selection (genetic algorithm), Computer science, Surprise, Problem statement, Benford's law, Accounting, Economics, Machine learning, Psychology, Mathematics, Management science, Statistics, Law, Political science, Physical therapy, Medicine, Mathematical analysis, Audit, Social psychology

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Cited by: 5

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