Keywords
Machine learning techniques
XGBoost
Creditworthiness
How to Cite
Abstract
This study aims to improve the prediction of personal loan eligibility through the application of advanced machine learning techniques. Accurate prediction of creditworthiness is crucial for financial institutions to mitigate risks and optimize their lending processes. We evaluated three algorithms Gradient Boosting, XGBoost, and AdaBoost using a comprehensive dataset containing demographic and banking information. Among these, XGBoost proved to be the most effective model, achieving an accuracy of 0.95, precision of 0.95, recall of 0.95, and an F1 score of 0.95. These results demonstrate XGBoost's superior ability to accurately identify individuals likely to repay loans, making it an invaluable tool for enhancing decision-making in loan approvals. By leveraging XGBoost, banks can reduce the risk of defaults, streamline their operations, and provide better customer service, ultimately leading to more efficient and reliable lending strategies.
References
Gerlein, E., T. McGinnity, A. Belatreche, and S. Coleman, 2016 Evaluating machine learning classification for financial trading: An empirical approach. Expert Systems with Applications 54: 193–207.
Henrique, B. M., V. A. Sobreiro, and H. Kimura, 2019 Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications 124: 226–251.
Hu, W., W. Hu, and S. Maybank, 2008 Adaboost-based algorithm for network intrusion detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 38: 577–583.
Kamalov, F., I. Gurrib, and K. Rajab, 2021 Financial forecasting with machine learning: price vs return. Journal of Computer Science 17: 251–264.
Li, W., Y. Yin, X. Quan, and H. Zhang, 2019 Gene expression value prediction based on xgboost algorithm. Frontiers in genetics 10: 1077.
Lin, W.-Y., Y.-H. Hu, and C.-F. Tsai, 2011 Machine learning in financial crisis prediction: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42: 421–436.
Mashrur, A., W. Luo, N. A. Zaidi, and A. Robles-Kelly, 2020 Machine learning for financial risk management: a survey. IEEE Access 8: 203203–203223.
Mitchell, R. and E. Frank, 2017 Accelerating the xgboost algorithm using gpu computing. PeerJ Computer Science 3: e127.
Mulvey, J. M., 2017 Machine learning and financial planning. IEEE Potentials 36: 8–13.
Natekin, A. and A. Knoll, 2013 Gradient boosting machines, a tutorial. Frontiers in neurorobotics 7: 21.
Shinde, P. P. and S. Shah, 2018 A review of machine learning and deep learning applications. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA), pp. 1–6, IEEE.
Vats, P. and K. Samdani, 2019 Study on machine learning techniques in financial markets. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–5, IEEE.
Ying, C., M. Qi-Guang, L. Jia-Chen, and G. Lin, 2013 Advance and prospects of adaboost algorithm. Acta Automatica Sinica 39: 745–758.
Zhang, Y. and A. Haghani, 2015 A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies 58: 308–324.
Zhou, Z.-H., 2021 Machine learning. Springer Nature.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.