基于数据挖掘的持卡人信用风险管理研究
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引用本文:陈为民,张小勇,马超群.基于数据挖掘的持卡人信用风险管理研究[J].财经理论与实践,2012,(5):36-40
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作者单位
陈为民,张小勇,马超群 (1.湖南科技大学 商学院 湖南湘潭411201
2.湖南大学 工商管理学院 湖南长沙410082) 
中文摘要:目前的信用卡信用风险研究主要是如何提高模型的预测准确率。针对银行信用卡数据的异质性和信用数据的高度非线性,本文提出了对持卡人信用风险管理的混合数据挖掘方法。该方法包含两个阶段,在聚类阶段,样本数据被聚成同质的类,删除孤立点,不一致样本点重置标签,使样本更具有代表性;在分类阶段,基于样本进行训练生成支持向量机分类器法,对待分样本分类。基于实际数据进行了数值实验,并根据各类样本的特点提出了相应的风险管理策略。
中文关键词:信用风险  风险管理  数据挖掘  聚类  支持向量机
 
Credit Risk Management of Cardholder Based on Data Mining
Abstract:How to increase the accuracy of the forecast model has been a hot issue in the credit risk management. Due to the heterogenicity and non-linearity of the credit data, a hybrid data mining technique combing SOM cluster and SVM classifier is proposed in the paper. There are two phrases in the research:in the clustering phrase, the samples are grouped into homogeneous clusters,and the isolated samples are deleted and inconsistent samples are relabeled. In the classification phrase, the scoring model has been built by the support vector machines with samples of new labels. Then experiment is done using the credit data provided by a local bank, and risk management strategies are developed according to the characteristics of data.
keywords:Credit Risk  Risk Management  Date Mining  Cluster  Support Vector Machines
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