基于GP的个人信用评估非线性组合预测模型

个人信用评估对于商业银行控制信贷风险具有重要意义。针对单一模型存在的分类精度不高的问题，需将组合预测模型用于个人信用评估。本文在线性回归和Logistic回归两种单一统计模型的基础上，利用遗传规划（GP）构建了一种非线性组合预测模型。将模型应用于某商业银行的消费信贷数据的分类，其结果表明，基于GP的非线性组合预测模型有效地提高了分类精度，模型的第二娄误判率低，对于商业银行控制个人信用风险具有更好的适用性。

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

Personal credit scoring plays an important role for commercial banks to control consumer credit risks. Aiming at the low predictive accuracies of single models, this paper presents a combining forecast model for personal credit scoring. Based on two single statistical models of linear regression and logistic regression, this paper constructs a non-linear combining forecast based on genetic programming （GP） and uses the constructed model to classify the consumer credit data of one commercial bank. The application results indicate that the non-linear combining forecast based on GP increases the predictive accuracy effectively and the model also gets a much lower type Ⅱ error rate which is more applicable for commercial banks to control consumer credit risks.