Based On Data Mining Technology, Personal Credit Scoring Model

With the development of personal consumption loans, commercial banks and its regulators pay more attention to the personal credit risks. Nowadays, personal credit risk evaluation which basically decided by experience or qualitative analysis reveals more disadvantages compared with commercial credit rating. Facing the surge of loan portfolios and the shortage of employees, it may result in prolonging of approval, increasing of faults, declining of service standard and risk control ability, thus leading to deterioration of asset quality, the loss of potential clients, and the decline of competitiveness. This paper develops a personal credit evaluating model for the purpose of improving efficiency and accuracy of lending, and enhancing the asset quality and risk control ability.By Contrasting and analyzing some other personal credit evaluating model at home and abroad, this paper presents a personal credit evaluating model based on data mining technology. It filters attributes by gain of information entropy, finding the attributes which are important to classification and prediction, so as to classify and predict by Bayesian Classifier. The model is tested and evaluated by data of individual housing loan of a bank. The results demonstrate that the deviation is low and the accuracy of prediction is better at the condition of insufficient original information.