Personal Credit Scoring Model of Non-linear Combining Forecast Based on GP

Aiming at low predictive accuracies of single models, this paper presents a combining forecast for personal credit scoring. Based on two single statistical models of linear regression and logistic regression, this paper constructed a non-linear combining forecast by genetic programming (GP) and used the constructed model for personal credit scoring. The application results indicate that the predictive accuracy of the non-linear combining forecast based on GP is higher than linear regression, logistic regression and the linear combining forecast based on least square method by 3.40%, 2.83% and 2.64% respectively. The non-linear combining forecast also gets a much lower type II error rate which is more significant for commercial banks to keep away from consumer credit risks.