Identifying the fraudulent financial information based on data classification method

Abstract

Finance fraud of companies is an international difficult problem with a long history. The finance fraud problem is concerned by lots of people. Some researchers make a lot of qualitative or quantitative researches and get some valuable conclusions. In this article , we mainly applies empirical research method, combined with normative research method. First of all, this paper reviews the relevant literatures of financial fraud detecting of listed companies, expounds existing research results from the aspects of motives, signs and detecting methods. We appraise these results are ordering to national conditions and characteristics, analyze the definition of financial fraud. We established a new method which is partial least squares (PLS) and support vector regression (SVR) to solve the above problem in finance. The PLS are able to reduce dimension effectively, acquire nonlinear factor matrix, and SVR has many advantages, such as high imitation degree, effective classification and strong robustness. The model which combines PLS and SVR has great recognition effect. © 2014 SERSC.