Akinjole, A.; Shobayo, O.; Popoola, J.; Okoyeigbo, O.; Ogunleye, B. Ensemble based Machine Learning Algorithm for Loan Default Risk Prediction. Preprints2024, 2024090923. https://doi.org/10.20944/preprints202409.0923.v1
APA Style
Akinjole, A., Shobayo, O., Popoola, J., Okoyeigbo, O., & Ogunleye, B. (2024). Ensemble based Machine Learning Algorithm for Loan Default Risk Prediction. Preprints. https://doi.org/10.20944/preprints202409.0923.v1
Chicago/Turabian Style
Akinjole, A., Obinna Okoyeigbo and Bayode Ogunleye. 2024 "Ensemble based Machine Learning Algorithm for Loan Default Risk Prediction" Preprints. https://doi.org/10.20944/preprints202409.0923.v1
Abstract
Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques and to also address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest, decision tree, SVM (Support Vector Machine), XGBoost (eXtreme Gradient Boosting), ADABoost (ADAptive Boosting) and multilayered perceptron with three-hidden layers, to predict credit default using loan data from LendingClub. Additionally, we also combined the model predictions using voting and stacking ensemble methods to enhance the models' performance. Furthermore, various sampling techniques was explored to handle the issue of class imbalance observed in the dataset, with the result showing that the balanced data performs better than the imbalanced data. Our proposed model achieved an accuracy of 93.7%, a precision of 95.6% and a recall of 95.5%, which shows the potential of ensemble methods in improving credit default predictions and can provide lending platforms with the tool to reduce default rates and financial losses. In conclusion, the findings from this study have broader implications for financial institutions, offering a robust approach to risk assessment beyond the Lend-ingClub dataset.
Keywords
Credit default prediction; deep learning; ensemble learning; machine leaning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
