Estimating the utility value of individual credit card delinquents

被引：7

作者：

Chung, Suk-Hoon ^{[1
]}

Suh, YongMoo ^{[1
]}

机构：

[1] Korea Univ, Sch Business, Seoul, South Korea

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 02期

关键词：

Utility data mining; Credit classification model; Neural networks; Decision tree; Credit card transaction data; SUPPORT VECTOR MACHINES; SCORING MODELS; NEURAL-NETWORKS; MINING APPROACH; ROUGH SET; CLASSIFICATION; PREDICTION;

D O I：

10.1016/j.eswa.2008.02.031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Excessive issue of credit cards has contributed to increased credit card delinquencies, which have become a burden for credit card companies. In such a negative situation, companies should build and use models to estimate maximum profits from credit card delinquents. However, traditional classification models used to classify customers into good or bad groups are not useful in estimating profits from credit card delinquents. Therefore, this paper suggests two models to estimate the utility value of individual credit card delinquents. After showing that the best classification model does not necessarily result in the best utility model, we explain a model that could be used to estimate utility value of individual credit card delinquents. Such models are expected to give much more value to the credit card companies than the traditional classification models. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：3975 / 3981

页数：7

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