텍스트 감성분석과 해석가능한 기계학습 기반의 포렌식 기법을 활용한 회계부정탐지 : 내부회계관리제도를 중심으로

This study tried to verify exploratorily whether the sentiment index of operation report of Internal Control over Financial Reporting (ICFR) has an additional contribution for developing accounting fraud detection models based on machine learning and to interpret the results. The sanction cases of Financial Supervisory Service (FSS) was used as a proxy for accounting fraud. Non-SA Set consists of comprehensive input variables, such as financial ratio and ICFR related variables (183) and SA Set is addition of the sentiment index of operation report of ICFR using Google's natural language process (NLP) API to Non-SA Set. The results of developing a total of 8 models by training 4 models on 2 each data sets are as follows. First, the logistic model based prediction model (LGST-SA) showed the highest predictive performance on SA Set. Second, the random forest model based prediction model (RF-NSA) showed the highest predictive performance on Non-SA Set. Third, the predictive performance of LGST-SA was significantly higher than that of RF-NSA, so the predictive contribution of the sentiment index was verified. Additionally, through SHAP, one of the interpretable machine learning (IML) methods, it was anecdotally confirmed that the sentiment index had a positive relationship on accounting fraud. This study has importance in that the first paper to verify the usefulness of the sentiment analysis of operation report of ICFR for developing an accounting fraud detection model and to suggest a way to overcome the limitations of the machine learning.