Using Machine Learning to Detect and Forecast Accounting Fraud

Satoshi Kondo, Daisuke Miyakawa, Kengo Shiraki, Miki Suga and Teppei Usuki

Discussion papers from Research Institute of Economy, Trade and Industry (RIETI)

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.

Pages: 62 pages
Date: 2019-12
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eti:dpaper:19103

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