An Application of Machine Learning Techniques for Loan Default Payment Prediction
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Abstract
In the banking business, predicting customer default payments has become a crucial operation to prevent and mitigate risks caused by non-performing loans. Presently, machine learning techniques are used alongside traditional methods for this task. This paper explores several ways to apply machine learning techniques in predicting default payments. The prediction development framework includes data encoding, data sampling, and model development. At each step, various techniques are tested and compared to find optimal solutions for business requirements. Our findings conclude that ensemble models are a good choice over a single model to increase the precision of the default payment class. The Over-sampling method is a suitable choice to increase recall of the default payment class, whereas the Under-sampling method is not recommended. Furthermore, if the size of the input vector is a concern, the Weight of Evidence encoding method can be used instead of One-hot encoding without a loss in performance.
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References
A. K. I. Hassan and a. Abraham, “Modeling Consumer Loan Default Prediction Using Ensemble Neural Networks”, International Conference on Computing, Electrical and Electronic Engineering (ICCEEE), Khartoum, Sudan, pp. 719-724, 2013.
T. Alam, K. Shaukat, I. A. Hameed, S. Luo, M. U. Sarwar, S. Shabbir, J. Li, and M. Khushi, “An Investigation of Credit Card Default Prediction in the Imbalanced Datasets”, IEEE Access, 8: 201173-201198, 2020.
A. Soni and K. C. P. Shankar, “Bank Loan Default Prediction Using Ensemble Machine Learning Algorithm”, 2nd International Conference on Interdisciplinary Cyber Physical Systems (ICPS), Chennai, India, pp. 170-175, 2022.
S. K. Shaheen and E. ElFakharany, “Predictive analytics for loan default in banking sector using machine learning techniques”, 28th International Conference on Computer Theory and Applications (ICCTA), Alexandria, Egypt, pp. 66-71, 2018.
S. Fan, “Design and implementation of a personal loan default prediction platform based on LightGBM model”, 3rd International Conference on Power, Electronics and Computer Applications, Shenyang (ICPECA), China, pp. 1232-1236, 2023.
L. Lai, “Loan Default Prediction with Machine Learning Techniques”, International Conference on Computer Communication and Network Security (CCNS), Xi'an, China, pp. 5-9, 2020.
S. Barua, D. Gavandi, P. Sangle, L. Shinde, and J. Ramteke, “Swindle: Predicting the Probability of Loan Defaults using CatBoost Algorithm”, 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 1710-1715, 2021.
A. Al-qerem, G. Al-Naymat, and M. Alhasan, “Loan Default Prediction Model Improvement through Comprehensive Preprocessing and Features Selection”, Arab Conference on Information Technology (ACIT), Al Ain, United Arab Emirates, pp. 235-240, 2019.
B. Patel, H. Patil, J. Hembram, and S. Jaswal, “Loan Default Forecasting using Data Mining”, International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1-4, 2020.
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