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.
References
[1]
US Census Bureau (2010) Table 1188. Usage of General Purpose Credit Cards by Families: 1995 to 2007.
https://www2.census.gov/library/publications/2010/compendia/statab/130ed/tables/11s1188.pdf
[2]
Wallet, N. (2019) American Household Credit Card Debt Study.
https://www.nerdwallet.com/blog/credit-card-data/
[3]
Stiglitz, W. (2001) The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2001: Information for the Public. The Royal Swedish Academy of Sciences, Nobel Foundation. nobelprize.org.
[4]
Merikoski, M., Viitala, A. and Shafik, N. (2018) Predicting and Preventing Credit Card Default.
[5]
Yeh, I.-C. and Lien, C.-H. (2009) The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients. Expert Systems with Applications, 36, 2473-2480.
https://doi.org/10.1016/j.eswa.2007.12.020
[6]
Venkattesh, A. and Jacob, G. (2016) Prediction of Credit-Card Defaulters: A Comparative Study on Performance of Classifiers. International Journal of Computer Applications, 145, 36-41. https://doi.org/10.5120/ijca2016910702
[7]
Ogundimu, E.O. (2019) Prediction of Default Probability by Using Statistical Models for Rare Events. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182, 1143-1162. https://doi.org/10.1111/rssa.12467
[8]
Sun, T. and Vasarhelyi, M. (2019) Predicting Credit Card Delinquency: An Application of the Decision Tree Technique. In: Dai, J., Vasarhelyi, M. and Medinets, A., Eds., Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry (Rutgers Studies in Accounting Analytics), Emerald Publishing Limited, 71-83. https://doi.org/10.1108/978-1-78743-085-320191006
Dataset from University of California, Irvine.
http://archive.ics.uci.edu/ml/index.html
[11]
Chen, T. and Guestrin, C. (2016) XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, 13-17 August 2016, 785-794.
https://doi.org/10.1145/2939672.2939785
[12]
Leow, M. and Crook, J. (2016) A New Mixture Model for the Estimation of Credit Card Exposure at Default. European Journal of Operational Research, 249, 487-497.
https://doi.org/10.1016/j.ejor.2015.10.001
[13]
Agarwal, S., Chomsisengphet, S., Liu, C., Song, C. and Souleles, N.S. (2018) Benefits of Relationship Banking: Evidence from Consumer Credit Markets. Journal of Monetary Economics, 96, 16-32. https://doi.org/10.1016/j.jmoneco.2018.02.005
