Personal Credit Loans Risk Prediction Based on NS3-LightGBM

DOI

10.2991/978-94-6463-546-1_7How to use a DOI?

Keywords

personal credit loans; risk prediction; machine learning

Abstract

In recent years, the demand for personal credit loans has been increasing day by day. For banks, how to accurately identify and effectively predict whether borrowers will repay on time is a highly concerning issue. To effectively address a series of key problems existing in traditional loan risk prediction models, such as insufficient prediction performance, single hyperparameter optimization objectives, and poor model interpretability, this research integrates machine learning algorithms such as LightGBM and NSGA-III and builds an algorithm called NS3-LightGBM for predicting the probability of borrowers repaying on time. Testing and empirical analysis are done to confirm the suggested model’s ability to make predictions. The suggested model has an accuracy rate of more than 86%, according to the results. in prediction, and its prediction ability is better than traditional machine learning models. In addition, through a more detailed analysis of the impact of various features on the prediction results, it is found that monthly income, the number of months on-time repayment per year, and whether there is a fixed job are key features that affect the possibility of borrowers repaying on time.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Juncheng Zhang
PY  - 2024
DA  - 2024/10/27
TI  - Personal Credit Loans Risk Prediction Based on NS3-LightGBM
BT  - Proceedings of the 2024 2nd International Conference on Finance, Trade and Business Management (FTBM 2024)
PB  - Atlantis Press
SP  - 52
EP  - 61
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-546-1_7
DO  - 10.2991/978-94-6463-546-1_7
ID  - Zhang2024
ER  -