Based On Support Vector Machine (SVM) Personal Credit Evaluation

Risk of major financial institutions is from credit risk.With business and personal applications-increasing loaning, the establishment of effective risk prevention mechanism of the banks is imminent. The subject of the Qilu commercial banks based integrated information platform, the platform of the individual credit risk assessment study conducted in-depth research, is proposed based on support vector machine model of individual credit risk assessment system. Integration of the database data mining, artificial intelligence and statistics, and other subjects, is a complex data from a large number of quick access to useful information in the new technology. Classification is one of the most common application of data mining the direction of the training received through the empirical data to predict the unknown data classification categories.Support Vector Machine (SVM) in recent years in statistical learning theory developed on the basis of a new machine learning method, which has strong generalization ability. The core idea is a complex classification task mapping to make it through the kernel function into a high dimensional feature space to construct the linear separating hyperplane problem. Support vector machine is a good problem to solve two new classification method, the structural stability of better learning outcomes model.This paper careful studys the theory of support vector machine . In large-scale data sets for support vector machine principle and algorithm research, by comparing the advantages and disadvantages of each algorithm, the choice of an improved sequential minimal optimization algorithm (SMO) to improve SVM based on individual credit evaluation model learning speed. SVM-based bank and the personal credit rating system model are introduced. Support vector machines applied to the evaluation of personal credit, the last experiment to prove that the model has a good effect.