SHS Web Conf.
Volume 181, 20242023 International Conference on Digital Economy and Business Administration (ICDEBA 2023)
|Number of page(s)
|Financial Analysis and Stock Market Strategies
|17 January 2024
Research on loan default prediction based on logistic regression, randomforest, xgboost and adaboost
Guangdong University of Technology, Guangzhou, 510006, China
Corresponding author: firstname.lastname@example.org
Lenders often experience loan defaults, resulting in huge losses to lenders. Lenders are required to conduct a credit assessment of borrowers before making loans. Machine learning plays an essential role in loan credit analysis. This study analyzes the application of machine learning in loan credit analysis through a dataset of borrowers from Kaggle and looks for an excellent algorithm.This study use Logistic Regression, randomforest, XGBoost and AdaBoost to fit the dateset and compare their accuracy in prediction.In terms of results, XGBoost performed well while logistic regression performed poorly. For banks or lending institutions, using Gradient Boosting Decision Tree like XGBoost to predict loan default can increase profit.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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