Abnormality Detection of ERP Financial Management System Based on Machine Learning
Financial management is related to financial security and has a significant impact on the normal operation of hospitals.Traditional financial management abnormality detection has the problems of strong subjectivity and low accuracy.Timely and accurate detection of hospital financial management system abnormality is crucial.This paper analyzes the modules of the hospi-tal ERP financial management system and constructs an abnormality detection model for the financial management system based on the D-S evidence theory.The proposed abnormality detection model for financial management systems is applied to analyze historical data in a third class hospital's financial management system.Compared with the support vector machine model,the results show that the system abnormality detection model constructed by D-S evidence theory has better abnormality detection performance,which provides a reference for improving the accuracy of abnormality detection in the hospital ERP financial man-agement system and ensuring the improvement of the hospital's financial management level.
financial management systemabnormality detectionD-S evidence theory
万方数据
维普
