German Credit Risk Prediction Using Machine Learning Models

DOI

10.2991/978-94-6463-823-3_27How to use a DOI?

Keywords

German Credit Risk Prediction; Machine Learning Models; ensemble methods

Abstract

Management of credit risk plays a vital role in the financial industry, allowing institutions to mitigate losses, optimize capital allocation, and make informed decisions. This study investigates the predictive efficacy of five machine learning algorithms (Decision Trees, Logistic Regression, Random Forest, k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) and three ensemble methods (voting, gradient boosting and stacking) in a German credit dataset. The results show that models have a better performance when data preprocessing is refined. For example, the accuracy of KNN is increased from 0.69 to 0.74. Besides, ensemble models outperform the best performance of the single algorithm. For example, the best-performing Xgboost reaches a higher F1 score (0.61) compared with Random Forest (0.6). However, to reach better performance, handling data imbalance and redundant noise should be taken into consideration. In general, by systematically comparing the boundary conditions of a single model and an integrated framework, this paper verifies the important role of data preprocessing and ensemble methods in credit risk assessment and provides a reproducible benchmark process for the construction of lightweight risk control systems.

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  - Rongfei Ma
PY  - 2025
DA  - 2025/08/31
TI  - German Credit Risk Prediction Using Machine Learning Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 283
EP  - 292
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_27
DO  - 10.2991/978-94-6463-823-3_27
ID  - Ma2025
ER  -