A Model for predicting pre-delinquency of credit card accounts using Extreme Gradient boosting

Abstract

Credit risk is one of the significant risks that financial institutions that advance credit in credit cards are exposed to. Credit card accounts are usually classified as “good” or “bad” depending on the propensity of the cardholder to settle their debt on time. The latter usually pose a significant negative impact to the issuer’s books when the credit card account falls into late collections and recoveries are futile resulting to bad debts. Ensemble classifier algorithms have demonstrated greater performance in classification and regression problems due to their ability to trade-off bias and variance factors. In this study, an Extreme Gradient Boosting ensemble classifier was implemented based on cardholder personal characteristics and transaction patterns with the aim to minimize defaults in the late collection stages by identifying credit card accounts that exhibit early signs of delinquency way before the cardholder misses payments. A credit card dataset from the UCI Machine Learning repository was used to train and validate the model, which achieved a prediction accuracy of 81.62% and outperformed a set of single classifiers that were used in benchmarking. Depending on each score, the issuer will make informed decisions of how well to proactively engage the cardholder to identify the best way of intervening in their financial situation and mitigate the risk of missing payments.