Forecasting Tax Risk by Machine Learning: Case of Firms in Ho Chi Minh City

Abstract

Tax is the main source of income for the State. However, managing tax collection effectively and limiting the tax risks is a challenge for state tax authorities. This study applies machine learning to assess and predict firms with tax risks using logistic regression algorithm. The data set includes 872 observations of firms in Vietnam market. The machine learning approach is used to classifies the firms into 2 categories which has tax risk or not based on 6 main factors: (i) revenue and other income; (ii) expenses; (iii) liquidity; (iv) asset; (v) liabilities; and (vi) equity. The results show that the machine learning method is effective and accurate in identifying and predicting risks in tax declaration. The authors recommend that the tax agencies could apply machine learning methods and go further with big data and artificial intelligence approach to identify and classify enterprises.