A k-nearest-neighbour classifier for assessing consumer credit risk

The last 30 years have seen the development of credit scoring techniques for assessing the creditworthiness of consumer loan applicants. Traditional credit scoring methodology has involved the use of techniques such as discriminant analysis, linear or logistic regression, linear programming and decision trees. In this paper we look at the application of the k-nearest-neighbour (k-NN) method, a standard technique in pattern recognition and nonparametric statistics, to the credit scoring problem. We propose an adjusted version of the Euclidean distance metric which attempts to incorporate knowledge of class separation contained in the data. Our k-NN methodology is applied to a real data set and we discuss the selection of optimal values of the parameters k and D included in the method. To assess the potential of the method we make comparisons with linear and logistic regression and decision trees and graphs. We end by discussing a practical implementation of the proposed k-NN classifier.