Improving the Accuracy of Bank Credit Assessment System Using Big Data Technology based on Decision Tree

DOI

10.2991/978-94-6463-992-6_27How to use a DOI?

Keywords

Big Data Technology; Bank Credit Assessment; Decision Tree; Hadoop HDFS; Class Imbalance Processing

Abstract

Traditional bank credit assessment methods rely on limited feature variables and static rules, which affects the accuracy and stability of credit assessment. To this end, this paper uses big data technology, combined with decision tree models and Hadoop HDFS storage and computing architecture to optimize the accuracy of bank credit assessment systems. Hadoop HDFS is used to distribute and manage large-scale credit data. The data block partitioning strategy is adopted to divide large-scale user credit data into 128MB data blocks, and three copies are configured to improve the fault tolerance of the system. This paper uses CART (Classification and Regression Trees) decision trees for classification, selects the optimal segmentation features based on the Gini coefficient, constructs a credit rating model, and uses cross-validation to optimize the parameters. The optimized Hadoop HDFS + CART model reduces the total misjudgment cost by 40 million yuan compared with the traditional credit scoring model, a decrease of 21.6%. This paper provides more scientific and efficient support for bank risk management and credit decision-making.

Copyright

© 2026 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  - Guofeng Bao
PY  - 2026
DA  - 2026/02/20
TI  - Improving the Accuracy of Bank Credit Assessment System Using Big Data Technology based on Decision Tree
BT  - Proceedings of the 2025 4th International Conference on Mathematical Statistics and Economic Analysis (MSEA 2025)
PB  - Atlantis Press
SP  - 285
EP  - 296
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-992-6_27
DO  - 10.2991/978-94-6463-992-6_27
ID  - Bao2026
ER  -