Application Of Data Mining Technology In Economic Responsibility Audit Of Leading Cadres In University

On December 8,2015,the General Office of the CPC Central Committee and the General Office of the State Council formally printed and distributed the "Framework Opinions on Perfecting Several Major Issues in the Audit System",requires that by 2020 form an audit supervision system that is in line with the modernization of the state governance system and governance capability.Also,they proposed that they should fully cover the fulfillment of economic responsibilities of public funds,state-owned assets,state-owned resources and leading cadres.The report of the 19 major congresses also proposes that we should perfect the assessment and evaluation mechanism for cadres.The audit of economic responsibility of leading cadres is one of the most important steps for the requirements.In the era of big data,auditors will face increasing and diversified audit data.Existing audit methods will not fully and efficiently support them in conducting audits.Therefore,the purpose of this paper is to explore the audit methods that can be used when faced with large number of multiple audit data.This article first analyzes the particularity of the economic responsibility audit of the leading cadres of colleges and universities compared with the audit of the general economic responsibilities,and sorts out and analyzes the existing problems in the audit methods,auditing efficiency and other aspects.Secondly,this paper proposes a method of auditing the economic responsibilities of college officials based on data mining.In light of the low efficiency of traditional auditing methods and the particularity of evaluation indicators for the auditing of leading cadres in colleges and universities,the automatic classification and visualization techniques in data mining are introduced to improve the efficiency of data analysis.And the specific process of the economic responsibility audit of college leaders based on data mining are described.Finally,on the basis of real data,this paper try to use data mining technology to solve the problems of low efficiency faced by the four indicators of the economic responsibility audit,including administrative planning,major economic decision-making,work performance and scientific research achievements of the leading cadres of colleges and universities.Specifically,this paper automatically classifies and visualizes financial accounting management data,charging management data,fixed asset management data,and publication data to quickly discover important data and abnormal data,thereby discovering that data that does not meet expectations or regulations,and achieving auditing objectives,or provide the basis and direction for further auditing procedures.Automated and intelligent auditing is the future direction of development.The method described in this paper can automate the preliminary analysis of audit data,thereby significantly improving the auditing efficiency of auditors in the face of massive heterogeneous and heterogeneous data,and support the automation and intelligence of the economic responsibility audit of the leading cadres of colleges and universities.