Deep Learning Optimization for Credit Risk Assessment in Supply Chain Finance Under the Game Theory Framework

DOI

10.2991/978-94-6239-602-9_16How to use a DOI?

Keywords

Supply Chain Finance; Credit Risk Assessment; Game Theory; Deep Learning; Attention-BiLSTM Model

Abstract

Supply chain finance, as a core financial innovation model to alleviate the financing difficulties of small and medium-sized enterprises (SMEs) and enhance supply chain coordination efficiency, the accuracy of its credit risk assessment directly determines the safety and sustainability of financial services. Traditional assessment methods have shortcomings such as insufficient characterization of multi-agent game behaviors and weak ability to capture non-linear risk factors. This paper deeply integrates game theory and deep learning to construct an optimized model for credit risk assessment in supply chain finance. Firstly, based on Stackelberg game theory, it analyzes the strategic interaction mechanism among core enterprises, SMEs, and financial institutions, and derives the profit functions and game equilibrium conditions of each agent. Secondly, it designs an Attention-Based Bidirectional Long Short-Term Memory (Attention-BiLSTM) model, incorporating key strategic variables obtained from game analysis into the input feature system. Finally, empirical tests are conducted using 5,000 actual transaction data from a supply chain finance platform. The results show that the proposed model is significantly superior to comparison models such as logistic regression and single LSTM in core indicators including accuracy and recall rate, and game features rank among the top in contribution to risk assessment. This study provides interdisciplinary theoretical support and practical solutions for the precise control of credit risks in supply chain finance.

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  - Jinzhi Yang
PY  - 2026
DA  - 2026/03/13
TI  - Deep Learning Optimization for Credit Risk Assessment in Supply Chain Finance Under the Game Theory Framework
BT  - Proceedings of the 2025 7th International Conference on Economic Management and Model Engineering (ICEMME 2025)
PB  - Atlantis Press
SP  - 158
EP  - 166
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-602-9_16
DO  - 10.2991/978-94-6239-602-9_16
ID  - Yang2026
ER  -