Construction of Accounting Fraud and Its Audit Countermeasure Model Based on Computer Technology

Abstract

A prediction model of financial fraud of listed companies based on machine learning method is proposed to predict financial fraud of listed companies. Using the data set of Chinese listed companies from 2000 to 2020 as observation samples, Benford’s Law, LOF local anomaly method and SMOTE oversample were adopted, grey samples were excluded, and characteristic variables were selected from five aspects: fraud motivation, solvency, profitability, cash flow and operating capacity. The financial fraud identification model Xscore is established based on the XGBoost method. The Xscore model can improve the accuracy of model prediction, and is superior to the Fscore model and Cscore model in accuracy, recall rate, AUC index, KS value, PSI stability, etc. It is more suitable for predicting the financial fraud of listed companies in China. The results of this study are helpful in promoting the research and application of artificial intelligence and machine learning in accounting, and provide references for promoting the disclosure of high-quality financial information by listed companies and maintaining the order of the capital market.