|Author Name||USUKI Teppei (KPMG AZSA LLC) / KONDO Satoshi (KPMG AZSA LLC) / SHIRAKI Kengo (KPMG AZSA LLC) / SUGA Miki (KPMG LLP) / MIYAKAWA Daisuke (Hitotsubashi University)|
|Creation Date/NO.||July 2019 19-J-039|
|Research Project||Study Group on Corporate Finance and Firm Dynamics|
|Download / Links|
In this paper, we examine to what extent the employment of machine learning technique contributes to better detection and prediction of corporate (i.e., firm-level) accounting fraud. The obtained results show, first, that the capacity to detect accounting fraud increases substantially by using the machine learning-based model. Second, a similar improvement in predictive power is also confirmed. Such higher performance is due to both the employment of the machine learning technique and the higher dimensions of predictors. Third, we also confirm that a larger variety of data, such as corporate governance-related variables, which have not necessarily been used as main predictors in the extant studies, contribute to better detection and prediction to some extent. These results jointly suggest the existence of various unexploited information sources which are potentially useful for the detection and prediction of corporate accounting fraud.