Reject inference in credit scoring using Semi-supervised Support Vector Machines

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• 作者：Zhiyong Li^a ; ^e ; ^{liz@swufe.edu.cn} ; Ye Tian^b ; ^e ; ^{tianye@swufe.edu.cn} ; Ke Li^c ; ^{likec@swufe.edu.cn} ; Fanyin Zhou^c ; ^{zfy@swufe.edu.cn} ; Wei Yang^d ; ^e ; ^{yangw@swufe.edu.cn}
• 关键词：Reject inference ; Credit scoring ; Semi-supervised Support Vector Machines ; Online lending ; Predictive accuracy
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：15 May 2017
• 年：2017
• 卷：74
• 期：Complete
• 页码：105-114
• 全文大小：1328 K
• 卷排序：74

文摘

Semi-supervised Support Vector Machines for reject inference are proposed. The method uses information of both the accepted and rejected applicants. The method deals with labelled and unlabelled classes of the outcome. The model is tested on real consumer loans with a low acceptance rate. Predictive accuracy is improved by the new model compared to traditional methods.