GraphCredit: Enhancing Credit Explainability via Gated Graph Reasoning and Causal LLM Attribution

Complex prediction models widely adopted in the field of financial risk control, despite their superior performance in credit default prediction accuracy, have long suffered from issues regarding the explainability of their predictive results. This makes it difficult to satisfy stringent requirements for regulatory compliance and algorithmic fairness. Current mainstream explainability techniques primarily focus on feature attribution and generally lack structured modeling and deep reasoning capabilities for complex community correlations between financial entities, thereby failing to reveal the transmission mechanisms of community-based risks. To this end, this paper proposes a explainability-enhanced framework, namely GraphCredit. Specifically, GraphCredit first extracts and quantifies the community risks of borrowers to construct a borrower-centric knowledge graph. Subsequently, GraphCredit employs a GraphSAGE model combined with a gating mechanism to achieve dynamic feature weighting and credit default prediction, achieving an average performance improvement of 8.70% compared with the SOTA models. Finally, leveraging Large Language Models (LLMs), the framework transforms the extracted complex risk evidence chains into logically clear natural language narrative reports that comply with regulatory standards. Experimental results demonstrate that the explainability score under both human and LLM evaluations increased by 17.98% compared with SHapley Additive exPlanations (SHAP). GraphCredit elevates the explainability of credit default prediction from traditional static “feature attribution” to a dynamic “risk narrative” dimension, providing a new paradigm that balances high precision with robust trustworthiness for human-AI collaboration in high-risk financial decision-making.