credit risk assessment

Nowadays, the three major credit risks at banks and financial institutions are effective in various models. These factors include the risk of customer failures, losses rate in case of failure sand outstanding debts. For the purpose of reducing credit risks for banks and financial institutes, it is first necessary to obtain financial and non-financial information with the customers to design and validate a model to assess the credit risk. The main objective of this study is utilizing data mining and knowledge extraction methods to creating a model for analyzing the credit risk factors as well as the causes of delay in installments. Many efforts have been carried out to reduce credit risk by a diversity of approaches such as statistics methods, operating research and financial theories, but lack of the efficient credit risk approaches is a major cause of bank failures and crises around the world.

在当今时代，银行及金融机构面临的三种主要信用风险在多种模型中均表现显著。这些风险因素涵盖了客户违约风险、失败情况下的损失率以及逾期债务。为降低银行及金融机构的信用风险，首要任务是收集与客户相关的财务与非财务信息，以此设计并验证一个评估信用风险的模型。本研究的主要目标是通过数据挖掘与知识提取方法，构建一个分析信用风险因素及其还款延迟原因的模型。尽管众多尝试通过统计方法、运筹学及金融理论等多样化途径来降低信用风险，但缺乏有效的信用风险处理方法仍是导致全球银行倒闭与金融危机的主要原因。