Multivariate kernel discrimination applied to bank loan classification

Abstract

The purpose of this paper is to apply a kernel discriminant analysis to classify bank loans and determine which loans are at risk of default. This study starts by introducing the concept of kernel density estimation, which is a widely used non-parametric technique to obtain an estimate for the probability density function. This procedure is based on two main parameters: the kernel function and the bandwidth, the latter being the crucial parameter. The multivariate kernel density estimator is later applied to discriminant analysis to obtain kernel discrimination. This is a method which classifies observations into a predetermined number of distinct and disjoint classes. Finally, we apply multivariate kernel discriminant analysis to a sample of bank loans to determine which loans can be classified as defaulted. This model can help predict the likelihood that future loans may default.