Prediction of Default Probability of Credit-Card Bills

Abstract

The credit-card industry has existed for decades and is a product both of changing consumer habits and improved national incomes. Both the number of card issuers and issuing banks, and transaction volumes themselves, have increased significantly. Nonetheless, with the increase of credit-card transactions, overdue amounts and delinquency rates of credit-card loans have also become problems that cannot be ignored. The successful resolution of this issue is central to the successful future development of the industry. In this work, we have presented a credit-score model to reflect the attributes and credit ratings of clients. We merge 23 variables from the original dataset and 25 additional financial features that are mined from the original financial variables, then apply to the XGBoost model. The model itself provides the 13 most significant variables by listing them according to the calculated scores. It then predicts the probability of individuals’ willingness to pay back a credit-card loan. At last, the default ratio will be converted to a credit-score system to understand the credit ratings of clients more intuitively. This model can make contributions to the resolution of default probability and is very helpful to the credit-card industry.

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