A Credit Card Default Prediction Method Based on CatBoost
- DOI
- 10.2991/978-94-6463-222-4_17How to use a DOI?
- Keywords
- credit default; CatBoost; feature engineering; machine learning
- Abstract
This paper presents a study on the prediction of credit card user default using the CatBoost model. The dataset used in this study is a credit card dataset from a financial institution. The dataset contains information about the credit card users such as their age, gender, credit limit, and payment history. The CatBoost model was used to predict the probability of default for each user. The results showed that the CatBoost model was able to accurately predict the probability of default for credit card users. And in the experiment, I found that the prediction effect of CatBoost model is better than that of XGBoost, Lasso, and LightGBM. The results of this study can be used to help financial institutions better manage their credit card portfolios and reduce the risk of default.
- Copyright
- © 2023 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.
Cite this article
TY - CONF AU - Yikai Zhao PY - 2023 DA - 2023/08/28 TI - A Credit Card Default Prediction Method Based on CatBoost BT - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023) PB - Atlantis Press SP - 178 EP - 184 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-222-4_17 DO - 10.2991/978-94-6463-222-4_17 ID - Zhao2023 ER -