Exploring the Performance of XGBOOST and Artificial Neural Network in Personal Credit Default Prediction: An Empirical Study-Reference-Cited by-同舟云学术

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Exploring the Performance of XGBOOST and Artificial Neural Network in Personal Credit Default Prediction: An Empirical Study

Published:2023-07-19 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
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Author:

Xueping Zhu¹,Samuri Suzani Mohamad¹,Adnan Muhamad Hariz Muhamad¹

Affiliation:

1. Universiti Pendidikan Sultan Idris,DILIGENT SIG, Faculty of Computing and Meta-Technology,Tanjong Malim, Perak,Malaysia,35900

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10252163/10252090/10252429.pdf?arnumber=10252429

Reference12 articles.

1. Transparency, auditability, and explainability of machine learning models in credit scoring

2. Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation

3. Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models

4. Machine Learning in Banking Risk Management: A Literature Review

5. Credit default prediction from user-generated text in peer-to-peer lending using deep learning;kriebel;European Journal of Operational Research,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Credit scoring: Does XGboost outperform logistic regression?A test on Italian SMEs;Research in International Business and Finance;2024-06

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