Credit Risk Identification of Bank Client Basing on Supporting Vector Machines

Support vector machines(SVM) is the activest study content in statistical learning theory. Identificating and evaluating the credit risk of bank client Using the technology of SVM has the features of simple arithmetic and high precision. This article firstly introduces the theories and methods of bank client credit risk identification classified basing on SVM, and uses index entropy weighing select method to filter credit risk indexes of bank. Using the tochnology of SVM, takes the example of credit risk identification of a commercial bank, selects the object of related credit data of 68 clients in spinning industry of this bank, inspects its credit risk conditions.