An Investigation Of The Effect Of Variable Reduction On Classification Accuracy Rates Of Consumer Loans

Abstract

The profitability of loan granting institutions depends largely on the institutions’ ability to accurately evaluate credit risk. Their goal is to maximize income by issuing as many good loans to consumers as possible while minimizing losses associated with bad loans. Financial institutions have been using various computational intelligence methods and statistical techniques to improve credit risk prediction accuracy. This paper examines historical data from consumer loans issued by a German bank to individuals. The data consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off and defaulted upon.

This paper examines and compares the classification effectiveness of four computational intelligence techniques: 1) logistic regression (LR), 2) neural networks (NNs), 3) support vector machines (SVM), and 4) k-nearest neighbor (kNN) on three data sets to predict whether a consumer defaulted or paid off a loan. The first data set contains a full set of 20 input variables. The second and third data sets contain a reduced set of ten and six variables, respectively. The results from computer simulation show a limited effect of variable reduction on improvement in the classification performance.