Stacking Ensemble Learning Approach for Credit Rating of Bank Customers

Qinyu Guo

2023

Abstract

The banking industry has experienced tremendous growth and change in recent years, creating new challenges and opportunities for credit assessment and management. In this context, accurately and efficiently assessing customer credit risks has become the key to the success of the banking business. A financial risk approval model based on stacking technology is proposed in response to this demand. The model starts by selecting a data set containing multiple bank user features. After a series of steps, such as data preprocessing, feature selection, preliminary model training, and model optimization, it finally forms a credit assessment model with high prediction accuracy. During the model training process, various machine learning algorithms were used for comparison, including neural networks, random forests, decision trees, naive Bayes, etc., and the algorithms were improved through stacking technology to achieve higher accuracy and Area Under Curve (AUC). In addition, based on the stacked model’s prediction results, each customer’s credit score is also calculated, and the distribution of customers with different credit score segments is displayed through visualization technology. This provides financial institutions with detailed information about their customers’ credit risks, helping them formulate more reasonable lending policies and interest rates. Experimental results show that compared with other models, the proposed superposition-based risk approval model improves the joint loan approval rate by about 6% on the actual data set, proving its effectiveness and feasibility in financial risk assessment.

Paper Citation

in Harvard Style

Guo Q. (2023). Stacking Ensemble Learning Approach for Credit Rating of Bank Customers. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 274-278. DOI: 10.5220/0012801200003885

in Bibtex Style

@conference{daml23,
author={Qinyu Guo},
title={Stacking Ensemble Learning Approach for Credit Rating of Bank Customers},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={274-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012801200003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Stacking Ensemble Learning Approach for Credit Rating of Bank Customers
SN - 978-989-758-705-4
AU - Guo Q.
PY - 2023
SP - 274
EP - 278
DO - 10.5220/0012801200003885
PB - SciTePress