World Scientific
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at customercare@wspc.com for any enquiries.
https://doi.org/10.1142/S0219649224500424Cited by:0 (Source: Crossref)

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.