The use of MSD model in credit scoring

Abstract

A credit scoring classification problem can be defined as a decision process in which information from application forms for new or extended credit is used to separate the applicants into good and bad credit risks. In the credit industry, it is important to find a method that optimally separates applicants into 'goods' and 'bads' as good classification models can provide competitive advantage. These classification models can be developed by statistical techniques (e.g. statistical discriminant analysis and logistic regression), neural networks and mathematical programming (MP) discriminant analysis methods, although MP methods are less widely used in practice in spite of their advantages, e.g. MP methods are non-parametric and desired classifier characteristics can be represented by constraints in the MP model. In this paper, a MP model is described and compared with other known methods, using real data. The MP model uses minimization of the sum of the deviations of misclassified observations from the discriminant function as its objective function. The performance of this MP model is evaluated on three datasets for credit card applications and is compared with the performance of a k-NN classifier, discriminant analysis, support vector machines and and logistic regression.

A multicriteria approach to manage credit risk under strict uncertainty

“…A major revision on this concept, its beginnings and the difficulties arising when developing accurate methods of scoring is made by Mester (1997). Credit scoring models are usually based on classification models rely on information from applicants to separate good and bad credit risks (Falangis, 2007;Shi and Xu, 2016). A closely related problem is loan optimization.…”

A clustering and selection based transfer ensemble model for customer credit scoring

“…The above models do not take the issue that the distributions of customer data are often highly imbalanced into account in customer credit scoring. The so-called class imbalance means that the applicants with bad credit constitute only a very small minority of the data (usually 2% of the total customers) [10]. For example, the Council of Mortgage Lenders, UK, reported that in the second quarter of 2010, the number of mortgages three or more months in arrears, i.e., default applicants with bad credit stood at 2.17% of total outstanding mortgages [11].…”