A comparative study of data mining methods in consumer loans credit scoring management

Abstract

Credit scoring has become a critical and challenging management science issue as the credit industry has been facing stiffer competition in recent years. Many classification methods have been suggested to tackle this problem in the literature. In this paper, we investigate the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets. Besides the well-known classification algorithms (e.g. linear discriminant analysis, logistic regression, neural networks and k-nearest neighbor), we also investigate the suitability and performance of some recently proposed, advanced data mining techniques such as support vector machines (SVMs), classification and regression tree (CART), and multivariate adaptive regression splines (MARS). The performance is assessed by using the classification accuracy and cost of credit scoring errors. The experiment results show that SVM, MARS, logistic regression and neural networks yield a very good performance. However, CART and MARS's explanatory capability outperforms the other methods.

On the suitability of resampling techniques for the class imbalance problem in credit scoring

“…When comparing the results given by logR in Table 3 with those of SVM in Table 4, it seems that the logistic regression model consistently performs better than the SVM approach, independently of the imbalance ratio. This finding is in agreement with the conclusions drawn in some previous studies (Baesens et al 2003, Xiao et al 2006, Kennedy et al 2010.…”

“…From the many comparative studies carried out (Baesens et al 2003, Huang et al 2004, Xiao et al 2006, Wang et al 2011, it is not possible to claim the superiority of a method over other competing algorithms regardless of data characteristics. For instance, noisy samples, missing values, skewed class distribution and attribute relevance may significantly affect the success of most prediction models.…”

Using semi-supervised classifiers for credit scoring

“…Based on average rank logistic regression performs best. This should come as no surprise as both (Baesens et al, 2003) and (Xiao et al, 2006) reported logistic regression as performing strongly when assessed using credit scoring data.…”

“…Another problem is authors' expertise in their own method and failure to undertake a corresponding effort with existing methods (Michie et al, 1994). Indeed, Thomas (2009) highlights that studies which have endeavoured to avoid the aforementioned problems (Baesens et al, 2003;Xiao et al, 2006) have reported that the differences between the performance of classification techniques were small and regularly not statistically significant. Great care and consideration was taken to avoid these issues in this work, details of which are given in Section 4.…”

Optional Insurance Compensation Rate Selection and Evaluation in Financial Institutions

“…Georgios Sarantopoulos (2003) presented a real-world application of a data mining approach to credit scoring, and pointed out that the decision tree showed a great improvement in performance compared to the current manual decisions [8]. Wen-bing Xiao & Qian Zhao (2006) investigated the performance of various credit scoring models and the corresponding credit risk cost for three real-life credit scoring data sets [9]. Xiaohua presented a data mining approach for analyzing retailing bank customer attrition, and demonstrated effectiveness and efficiency by applying data mining technology in retailing bank [10].…”