基于不均衡支持向量机的小企业信用风险评价理论与模型

项目名称： 基于不均衡支持向量机的小企业信用风险评价理论与模型

项目编号： No.71201018

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 管理科学与工程

项目作者： 程砚秋

作者单位： 东北财经大学

项目金额： 22万元

中文摘要： 小企业信用风险评价既是银行风险管理问题，又事关经济社会稳定。针对小企业贷款中，违约样本远少于非违约样本、违约客户误判对银行影响较大的现实，鉴别小企业信用风险的关键影响要素，采用不均衡支持向量机进行评价，分析外部冲击对小企业信用风险的影响程度。根据特定指标对小企业违约状态的影响，保留能清晰鉴别客户违约与否的指标。根据样本差距越大、权重越大的赋权思路，采用不均衡支持向量机构建具有显著区分违约能力的评价方程。以违约判别能力作为模型检验标准，改变样本数据不均衡所导致的样本总体精度很高、违约样本精度反而不高的现象。给违约样本赋予大于非违约样本的权重，体现违约样本误判对银行影响较大的原则。通过宏观环境、行业环境等外部冲击与小企业信用风险评价得分的函数关系，揭示过去和现在的外部冲击对未来小企业信用风险的影响。本研究致力于提供一类基于不均衡数据的小企业信用风险评价方法，补充改善小企业风险评价理论和模型。

中文关键词： 信用风险评价；小企业贷款；不均衡数据；支持向量机；

英文摘要： Small business credit risk evaluation is an issue of banks' risk management, and is also related to the economic and social stability. For small business loans, the default samples are far less than the non-default samples. Also, the impact on banks of default customers' misjudgment is much larger than the impact on banks of non-default customers' misjudgment. In this case, this research will identify small business credit risk element, use the unbalanced support vector machine to evaluate the credit risk, analysis the impact of external shocks to small business credit risk. According to the impact of specific indicator to small businesses customer defaults or not, the invalid indicators which can not divide the default customers and the non-default customers will be removed. Then the factors of small business credit risk will be identified. According to the principle that the greater the gap between the default customers and the non-default customers, the greater the weight, we will establish an evaluation equation with significant distinction based on unbalanced support vector machine.Using the discriminated ability as the evaluation model test standard, we will change the phenomenon that the overall accuracy is high, but the default samples' accuracy is not high, which is cased by the unbalanced data. Giving

英文关键词： Credit Rating Evaluation；Small Enterprises’ loans；Unbalanced Data；Support Vector Machines；