Abstract
The most commonly used methods in predicting credit card defaulters are credit scoring models. Based on their applications in credit management, the scoring models can be classified into two categories: the first category concerns about application scores and the second category concerns behavior scores. Specifically, behavior scores are used to determine “raising or lowering the credit limit; how the account should be treated with regard to promotional or marketing decisions; and when action should be taken on a delinquent account”. Behavior scoring models utilize various techniques to identify attributes that can effectively separate credit cardholders behaviors. In this chapter, using the real-life credit card dataset, we first conduct the MCQP classification, then compare the performance of MCQP with MCLP, linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), and neural network (NN) methods in terms of predictive accuracy.
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Shi, Y., Tian, Y., Kou, G., Peng, Y., Li, J. (2011). Personal Credit Management. In: Optimization Based Data Mining: Theory and Applications. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-0-85729-504-0_13
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DOI: https://doi.org/10.1007/978-0-85729-504-0_13
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