Research on Detection of False Disclosure Statement of Japanese Companies:Method by Machine Learning using Mahalanobis Distance

Abstract

The purpose of this study is construction of the prediction model to discriminate incorrect accounting information. Two features of this research are to adopt methods of detecting auditing practices and to target for analysis that the accounting information which the sales are overestimated. Specifically, unlike in previous research, we approach to detect fraudulent means without uniform accounting phenomena for each fraudulent means. Furthermore, we applied accounting distortions and discomfort auditors feel as explanatory variables. This discomfort is measured by Mahalanobis distance. In the results of this analysis, the prediction model of machine learning that is adopted practical methods that detect incorrect accounting shows a high probability of fraud.