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Constructing A Reassigning Credit Scoring Model

机译：构建再分配信用评分模型

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摘要

Credit scoring model development became a very important issue as the credit industry has many competitions and bad debt problems.Therefore,most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years.In order to solve the classification and decrease the Type I error of credit scoring model,this paper presents a reassigning credit scoring model (RCSM) involving two stages.The classification stage is constructing an ANN-based credit scoring model,which classifies applicants with accepted (good) or rejected (bad) credits.The reassign stage is trying to reduce the Type I error by reassigning the rejected good credit applicants to the conditional accepted class by using the CBR-based classification technique.To demonstrate the effectiveness of proposed model,RCSM is performed on a credit card dataset obtained from UCI repository.As the results indicated,the proposed model not only proved more accurate credit scoring than other four common used approaches,but also contributes to increase business revenue by decreasing the Type I and Type II error of scoring system.
机译：由于信用行业竞争激烈，坏账问题严重，信用评分模型的发展成为一个非常重要的问题。因此，近年来，大多数信用评分模型在统计领域得到了广泛的研究，以提高信用评分模型的准确性。为了解决分类问题并减少信用评分模型的第一类错误，本文提出了一个重新分配的信用评分模型（RCSM），该过程分为两个阶段。分类阶段是构建基于ANN的信用评分模型，对合格的申请人进行分类。 （良好）或被拒绝（不良）信用。重新分配阶段正尝试通过使用基于CBR的分类技术，将被拒绝的良好信用申请人重新分配到条件接受类别中，以减少I类错误。为证明所提出模型的有效性， RCSM是对从UCI存储库中获得的信用卡数据集执行的。结果表明，该模型不仅证明了更准确与其他四种常用方法相比，它不仅可以进行信用评分，还可以通过减少评分系统的I类和II类错误来增加业务收入。

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