Model for Predicting Bank Loan Default using XGBoost
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10.5120/ijca2021921705 |
Omogbhemhe M.I. and Momodu I.B.A.. Model for Predicting Bank Loan Default using XGBoost. International Journal of Computer Applications 183(32):1-4, October 2021. BibTeX
@article{10.5120/ijca2021921705,
author = {Omogbhemhe M.I. and Momodu I.B.A.},
title = {Model for Predicting Bank Loan Default using XGBoost},
journal = {International Journal of Computer Applications},
issue_date = {October 2021},
volume = {183},
number = {32},
month = {Oct},
year = {2021},
issn = {0975-8887},
pages = {1-4},
numpages = {4},
url = {http://www.ijcaonline.org/archives/volume183/number32/32134-2021921705},
doi = {10.5120/ijca2021921705},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Loan default prediction is one of the most important and critical problem faced by many banks and other financial institutions as it has a huge effect on their survival and profit. Many traditional methods exist for mining information about a loan application and have been greatly studied and applied in the past. These methods seem to be underperforming as there have been reported increases in the amount of bad loans and defaulters among many financial institutions. In this paper, gradient boosting algorithm called XGBoost was used for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant. Similarly, important evaluation metrics such as Accuracy, Recall, precision, F1-Score and ROC area of the analysis were used. The paper provides an effective basis for loan credit approval in order to identify risky customers from a large number of loan applications using predictive modeling. The full utilization of this model will assist financial institutions in knowing a risking customer that may default in loan payment before lending.
References
- Manjeet K, Vishesh G, Tarun J, Sahil S and Lalit M. G. (2018). Neural Network Approach To Loan Default Prediction, International Research Journal of Engineering and Technology (IRJET) , p-ISSN: 2395-0072.
- Chiang, R. C., Chow, Y. F., & Liu, M. (2002). Residential mortgage lending and borrower risk: The relationship between mortgage spreads and individual characteristics. Journal of Real Estate Finance and Economics, 25(1), 5–32.
- Steenackers, A., & Goovaerts, M. J. (1989). A credit scoring model for personal loans. Insurance: Mathematics and Economics, 8(1), 31–34.
- Li Y. (2018). Research on bank credit default prediction based on data mining algorithm. The International Journal of Social Science and Humanities Invention 5(06): 4820-4820, ISSN: 2349-2031
- Ali B. (2006). Predicting Mortgage Loan Default with Machine Learning Methods.
- Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics and Data Analysis, 50, 111.
- Freund, Y., & Schapire, R. (1996). Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156 Bari, Italy.
- Zhou Z.H and Li. M(2012) Ensemble Methods Foundations and Algorithms, -13: 978-1-4398-3005 -5.
- Jerome H. F (1999). Greedy Function Approximation: A Gradient Boosting Machine, IMS 1999 Reitz Lecture.
- Tianqi C and Carlos G. (2016). XGBoost: A scalable tree boosting system, arXiv:1603.02754[cs.LG]
Keywords
XGBoost, Bank Loan Default