Comparative study of individual and ensemble methods of classification for credit scoring

Abstract

Credit Scoring is the primary method for classifying loan applicants into two classes, namely credible payers and defaulters. In general, credit score is the primary indicator of creditworthiness of the person. This credit scoring technique is used by banks and other money lenders to build a probabilistic predictive model, called a scorecard for estimating the probability of defaulters. In the current global scenario, credit scoring is a major tool for risk evaluation and risk management for all the existing and emerging economies. With the introduction of Basel II Accord, Credit scoring has gained much significance in retail credit industry. In this paper, we performed an extensive comparative in order to classify the credit scoring and identification of best classifier. Furthermore, we used two different categories of classifiers i.e. individual and ensemble. Identification of optimal machine-learning methods for credit scoring applications is a crucial step towards stable creditworthiness of the person. Different parameters Accuracy, AUC, F-measure, precision and recall are used for the evaluation of the results.