Dynamic Graph Learning with Static Relations for Credit Risk Assessment
Qi Yuan, Yang Liu, Yateng Tang, Xinhuan Chen, Xuehao Zheng, Qing He, Xiang Ao
Abstract
Credit risk assessment has increasingly become a prominent research field due to the dramatically increased incidents of financial default. Traditional graph-based methods have been developed to detect defaulters within user-merchant commercial payment networks. However, these methods face challenges in detecting complex risks, primarily due to their neglect of user-to-user fund transfer interactions and the under-utilization of temporal information. In this paper, we propose a novel framework named Dynamic Graph Neural Network with Static Relations (DGNN-SR) for credit risk assessment, which can encode the dynamic transaction graph and the static fund transfer graph simultaneously. To fully harness the temporal information, DGNN-SR employs a multi-view time encoder to explore the semantics of both relative and absolute time. To enhance the dynamic representations with static relations, we devise an adaptive re-weighting strategy to incorporate the static relations into the dynamic representations of time encoder, which extracts more discriminative features for risk assessment. Extensive experiments on two real-world business datasets demonstrate that our proposed method achieves a 0.85% - 2.5% improvement over existing SOTA methods.
BibTeX
@article{Yuan_Liu_Tang_Chen_Zheng_He_Ao_2025, title={Dynamic Graph Learning with Static Relations for Credit Risk Assessment}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/33433}, DOI={10.1609/aaai.v39i12.33433}, abstractNote={Credit risk assessment has increasingly become a prominent research field due to the dramatically increased incidents of financial default. Traditional graph-based methods have been developed to detect defaulters within user-merchant commercial payment networks. However, these methods face challenges in detecting complex risks, primarily due to their neglect of user-to-user fund transfer interactions and the under-utilization of temporal information. In this paper, we propose a novel framework named Dynamic Graph Neural Network with Static Relations (DGNN-SR) for credit risk assessment, which can encode the dynamic transaction graph and the static fund transfer graph simultaneously. To fully harness the temporal information, DGNN-SR employs a multi-view time encoder to explore the semantics of both relative and absolute time. To enhance the dynamic representations with static relations, we devise an adaptive re-weighting strategy to incorporate the static relations into the dynamic representations of time encoder, which extracts more discriminative features for risk assessment. Extensive experiments on two real-world business datasets demonstrate that our proposed method achieves a 0.85% - 2.5% improvement over existing SOTA methods.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yuan, Qi and Liu, Yang and Tang, Yateng and Chen, Xinhuan and Zheng, Xuehao and He, Qing and Ao, Xiang}, year={2025}, month={Apr.}, pages={13133-13141} }