|Author Name||KONDO Satoshi (KPMG AZSA LLC) / MIYAKAWA Daisuke (Hitotsubashi University) / SHIRAKI Kengo (KPMG AZSA LLC) / SUGA Miki (KPMG LLP) / USUKI Teppei (KPMG AZSA LLC)|
|Creation Date/NO.||December 2019 19-E-103|
|Research Project||Study Group on Corporate Finance and Firm Dynamics|
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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.
This is the English version of the Japanese Discussion Paper (19-J-039) with some additional information and changes.