Application of Machine Learning in Loan Default Prediction
DOI:
https://doi.org/10.54097/75k4fe13Keywords:
Default Prediction Model, Random Forest, Genetic Algorithms.Abstract
Loan default prediction is critical for financial risk management, enabling institutions to make informed lending decisions and mitigate potential losses. This study aims to improve the accuracy of loan default prediction using advanced machine learning techniques. Our research objectives include developing a robust prediction model through comprehensive data analysis, feature engineering, and model tuning. Methodologically, we use iterative interpolators to handle missing values, KBinsDiscretizer for feature binning, and neural networks optimized using Bayesian methods and genetic algorithms. The results show that the optimized model can produce more accurate prediction results.
References
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Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. doi:10.1145/2939672.2939785.
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