自上个世纪90年代以来，随着中国经济的发展和内需的扩大，信用卡业务迅速发展起来。信用卡业务的关键在于信用风险的控制，为了更好的防范信贷风险和进一步推动个人消费信贷业务的发展，发卡机构必须建立一套完善有效、科学合理的个人信用评价体系。信用评价将客户分为“好”客户和“差”客户，根据历史上每个属性的若干样本，建立数学模型，预测信用卡使用者的违约风险。决策树算法简单直观，误差率低，本文选取该算法作为建立信用评价模型的方法。 本文首先对数据挖掘、信用卡业务、信用评价三者的理论框架进行结合，分析了数据挖掘、信用卡业务中信用评价的特点，为下文建模方法的筛选奠定理论基础。其次结合数据挖掘几种方法的不同特性，选择决策树C5.0算法作为本文模型建立方法，并详细介绍了如何利用C5.0算法建立模型。最后从某发卡行获取的商业银行信用卡个人数据出发，采用上述算法，经过商业理解、数据理解、数据准备、建立模型、模型评估与分析等步骤，建立了个人信用评价的决策树模型。并且依据发卡机构的实际需求，对决策树模型的成本矩阵和修剪程度进行了调整，形成了修正改进的模型。 基于决策树方法的个人信用评价模型精确度高、可控性强，可在实际中广泛运用。且通过建立误判矩阵，使得发卡机构运营中成本最低。在实际运用中对于进行信用卡申请者的判别有一定的指导性作用，并能够为信贷决策提供支持，具有较强的理论和现实意义。

Since. the 90’s, with the development of China's economy and the expansion of domestic demand, Credit Card Industry developed rapidly. The key of Credit Card Industry is control of credit risks. In order to guard against credit risks efficiently, to promote the development of the consumer credit business, card issuers must establish a set of effective scientific and reasonable individual credit system. The credit rating system divided customers into “good” customers and ”bad” customers. Then according to the historical data of different attributes, set a mathematical model, forecast the default risk of credit card users. Decision Trees is simple, easily understanding and has low error rate, so this paper take the algorithm as the modeling method. Firstly, this Paper combines data mining, credit card industry, credit rating together, analyzed the characteristics of data mining and credit rating for credit card, lay a theoretical foundation for the following discussion. Secondly, consider the different feature of means of data mining, choose the algorithm C5.0 of Decision Tree as the following data mining mean, then introduce the specific processes of modeling with C5.0. Finally, make use of the individual credit card data which comes from certain bank, go through business Understanding, data understanding, data preparation, modeling, modeling evaluation, set a model based on decision tree. Then according to the specified actual demand of card issuer, adjust the Cost-sensitive Tree and prune decision tree, form a advanced model. Credit rating of consumer client of credit card based on decision tree has higher accuracy and stronger controllable nature, which can be used widely, and attributable to the cost-sensitive tree, the cost of card issuer is minimize. It has directive function in practice for distinguishing credit card applicant, It has strong theoretic and practical significance to provide support for credit decisions.