@article{1989,
author = "Yifan Huang and Yanxi Shao and Dapeng Tang and Jie Huang and Sijia Chen",
abstract = "In recent years, with the booming development of modern technology and information technology, intelligent risk control has become an indispensable part of the healthy development of the financial industry. The core issue in the field of intelligent risk management is to accurately identify potential risks in loans, not to issue loans to borrowers with a high default rate, or to track users who have issued loans in real time, so as to more effectively ensure the interests of lending institutions. Therefore, it is a very important research topic to use the massive data information of lenders and data mining technology to predict the default behavior of loan users. According to the characteristics of unbalanced loan data categories and high feature dimensions, we clean the data and select the features with strong predictive ability by filtered feature selection. Subsequently, based on the comparison of various models, this paper selects the best performing Adaboost model for loan default prediction model construction and conducts model evaluation. The analysis found that all the indicators of Adaboost are high, indicating that the model has better performance and can be used to accurately predict loan defaults. This is used as a basis to provide reference for commercial banks and other lending platforms when offering credit products to borrowers.",
issn = "23495219",
journal = "IJIRES",
keywords = "Loan Default Prediction, Filtered Feature Selection, Model Comparison, Adaboost",
month = "May",
number = "3",
pages = "149-159",
title = "{L}oan {D}efault {P}rediction {B}ased on {E}nsemble {L}earning",
volume = "10",
year = "2023",
}