Prediction Model for Loan Default Using Machine Learning

Abstract

Lack of appropriate predictive models for prompt loan assessment in granting loan application leads most financial lending institutions to bankruptcy. Loan default predictions models are used as critical tools in loan application assessment to discriminate between ‘bad' and ‘good' loan applicants. Recently, classification and survival techniques have been applied in loan default prediction with revolving cases of default over prediction. Despite advancements in automating decision-based loan systems, most existing models do not consider the ‘early loan repayment' attribute as a factor in resolving this prediction error. In practice, the adjustment for early loan repayment in model building is necessary because a high rate of early loan repayment decreases the number of defaults observed in a portfolio. To achieve this, three supervised machine learning algorithms i.e. logistic regression, Support vector machine and Naí¯ve B were used to develop a credit scoring models which include the early loan repayment attribute. The models were trained and tested on a loan dataset consisting of attributes with, and without early loan repayment attribute. Models evaluations were done using five performance metrics. The results of the performance evaluation shows that models that account for early loan repayment have higher accuracy, recall, precision, RMSE and ROC values than models trained without the early loan repayment attribute. The Logistic regression algorithm outperformed all other algorithms with 91% accuracy, 11% RMSE, 89% precision and 88% recall values.