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Using Machine Learning to Detect and Forecast Accounting Fraud

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  • KONDO Satoshi
  • MIYAKAWA Daisuke
  • SHIRAKI Kengo
  • SUGA Miki
  • USUKI Teppei

Abstract

This study investigates the usefulness of machine learning methods for detecting and forecasting accounting fraud. First, we aim to "detect" accounting fraud and confirm an improvement in detection performance. We achieve this by using machine learning, which allows high-dimensional feature space, compared with a classical parametric model, which is based on limited explanatory variables. Second, we aim to "forecast" accounting fraud, by using the same approach. This area has not been studied significantly in the past, yet we confirm a solid forecast performance. Third, we interpret the model by examining how estimated score changes with respect to change in each predictor. The validation is done on public listed companies in Japan, and we confirm that the machine learning method increases the model performance, and that higher interaction of predictors, which machine learning made possible, contributes to large improvement in prediction.

Suggested Citation

  • KONDO Satoshi & MIYAKAWA Daisuke & SHIRAKI Kengo & SUGA Miki & USUKI Teppei, 2019. "Using Machine Learning to Detect and Forecast Accounting Fraud," Discussion papers 19103, Research Institute of Economy, Trade and Industry (RIETI).
    • Handle: RePEc:eti:dpaper:19103
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    File URL: https://www.rieti.go.jp/jp/publications/dp/19e103.pdf
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    References listed on IDEAS

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    3. Song Mingzi & Naoto Oshiro & Akinobu Shuto, 2016. "Predicting accounting fraud: Evidence from Japan (Accepted by The Japanese Accounting Review)," CARF F-Series CARF-F-402, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Silke Janitza & Ender Celik & Anne-Laure Boulesteix, 2018. "A computationally fast variable importance test for random forests for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 885-915, December.
    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.
    6. Mingzi Song & Naoto Oshiro & Akinobu Shuto, 2016. "Predicting Accounting Fraud: Evidence from Japan," The Japanese Accounting Review, Research Institute for Economics & Business Administration, Kobe University, vol. 6, pages 17-63, December.
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    Cited by:

    1. Vitezslav Titl & Deni Mazrekaj & Fritz Schiltz, 2024. "Identifying Politically Connected Firms: A Machine Learning Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 137-155, February.

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