Research On The Model Of Bank Credit Risk Evaluation Based On SVM

The bank risk management has been the focus which the international and domestic financial circles have paid attention to all the time. After joining the WTO, the domestic banks are faced with the severe competition from the domestic and international banks. Since the means of credit decision support in the domestic banks can't keep up with the rapid changes in the business operation and market information, the proportion of the non-performing assets and loan is quite high, and the assets security of credit is poor, as well as the loan losses are serious. So a tremendous crisis is hided in the above-noted problems. A common research subject by domestic financial circles faced is how to establish an efficiency enterprise credit evaluation system and a good bank credit risk evaluation model, so as to provide the scientific quantized reference for making decisions in the banks, reduce non-performing loan rate in an all-round way and improve credit assets quality. Hence, it is great realistic significance to study the bank credit risk evaluation.The risk quantization model combining qualitative analysis with quantitative analysis is adopted by most banks to evaluate the credit risk currently. The risk rank division of the bank credit is a problem of multi-classed. The accuracy of credit predicted and the ability of model evaluating are affected directly by the modeling techniques. This paper makes deeply research on the modeling techniques of bank credit risk evaluation adopted by international and domestic. Aiming at the poor Generalization Ability and the long prediction time in using Back-Propagation NN to establish the model of bank credit risk evaluation, in the meantime, considering that Support Vector Machine can solve some problems of multi-classed; the paper adopts SVM to establish this model. However, the algorithm about structuring multi-classifier based on bi-classifier is adopted widely in using SVM to solve these problems. But the relation between two classes is considered only by the above-noted algorithm rather than the relation among other classes, so losing information among classes is inevitable. The information loss results in the decreases of accuracy classed necessarily. To improve the performance, the new algorithm about structuring multi-classifier based on Support Vector Regression is put forward in this paper. Bi-classifier is expanded to tri-classifier bringing about strengthen relation among classes and reduce information loss in the view of decision function, then tri-classifier is replaced by SVR so as to solve the problem of high computational cost caused by adjusting overabundant parameters. Finally, the facts and proposed algorithm results are compared by much test data provided by Bank of China binhe sub-branch. The experimental results show that the algorithm can improve efficiently the forecast accuracy, solve the problem of high computational cost, decrease the memory occupied, and increase efficiency. Therefore, it can offer more dependable reference for efficient and effective evaluation in bank credit risk.