A Meta-learning approach to Predict Non-performing Loans in Sri Lankan Financial Institutions

Abstract

Most financial institutions make the majority of their income from loan interest. However, due to the current financial crisis in Sri Lanka, non-performing loans have been the focus of financial industry concerns. Before the crisis, financial institutions were more ready to lend to businesses and individuals. Since the crisis, the rate of non-performing loans has increased, limiting the company’s growth. Predicting the likelihood of nonperforming loans can help in lowering this credit risk. Therefore, this paper presents a machine learning approach to predict nonperforming loans of a financial company in Sri Lanka. Moreover, this study also attempts to develop a meta-model that combines various classifiers, including K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The meta-model is also compared with the baseline models. The predictive performance of all models is compared using accuracy, precision, recall, F1-score, and AUROC score. At the end of the study, it was identified that the meta-learning model is the most effective model to handle this case, with a classification accuracy of 93.09%, precision of 82.68%, recall of 92.17%, F1 score of 86.24%, AUROC score of 96.0%, sensitivity of 92.44% and specificity of 93.17%.