A Comparative Study of Credit Scoring Machine Learning Models Based on Financial Indicators

DOI

10.2991/978-94-6463-823-3_23How to use a DOI?

Keywords

Financial Index; Machine Learning; Credit Scoring Model; Model Comparison; Financial Risk

Abstract

Accurate credit scores are crucial for financial institutions to effectively manage risks, optimize credit decisions. The traditional credit scoring model has its limitations in the face of massive and complex data. With its powerful data processing and pattern recognition capabilities, machine learning technology brings innovation opportunities to the field of credit scoring. This paper mainly conducts a comparative study on the machine learning credit scoring models of financial indicators of four financial institutions, namely HSBC, ICBC, BoA and Volksbank. Literature research method is comprehensively applied to sort out relevant theories and existing achievements, and the empirical analysis method is adopted to compare the performance of various common machine learning models, such as Logistic Regression, Decision Tree and Neural Network, in credit scoring based on actual financial data. The results show that different models have advantages and disadvantages in accuracy, recall rate, Area Under the ROC Curve(AUC) value, and other evaluation indices. The research conclusion shows that there is no absolute optimal model, and financial institutions should reasonably select or combine models according to their own data characteristics and business needs to improve the accuracy and reliability of credit scores.

Copyright

© 2025 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  - Yuqing Zhong
PY  - 2025
DA  - 2025/08/31
TI  - A Comparative Study of Credit Scoring Machine Learning Models Based on Financial Indicators
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 245
EP  - 253
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_23
DO  - 10.2991/978-94-6463-823-3_23
ID  - Zhong2025
ER  -