In the domain of corporate credit rating, traditional deep learning methods have improved predictive accuracy but still suffer from the inherent 'black-box' problem and limited interpretability. While incorporating non-financial information enriches the data and provides partial interpretability, the models still lack hierarchical reasoning mechanisms, limiting their comprehensive analytical capabilities. To address these challenges, we propose CreditXAI, a Multi-Agent System (MAS) framework that simulates the collaborative decision-making process of professional credit analysts. The framework focuses on business, financial, and governance risk dimensions to generate consistent and interpretable credit assessments. Experimental results demonstrate that multi-agent collaboration improves predictive accuracy by more than 7% over the best single-agent baseline, confirming its significant synergistic advantage in corporate credit risk evaluation. This study provides a new technical pathway to build intelligent and interpretable credit rating models.

翻译：在企业信用评级领域，传统深度学习方法虽提升了预测准确性，但仍存在固有的“黑箱”问题与可解释性不足的局限。尽管引入非财务信息丰富了数据并提供了部分可解释性，现有模型仍缺乏层次化推理机制，限制了其综合分析能力。为应对这些挑战，本文提出CreditXAI——一种模拟专业信用分析师协同决策过程的多智能体系统框架。该框架聚焦业务风险、财务风险与治理风险维度，以生成一致且可解释的信用评估。实验结果表明，多智能体协作相比最佳单智能体基线将预测准确率提升超过7%，证实了其在企业信用风险评估中显著的协同优势。本研究为构建智能化、可解释的信用评级模型提供了新的技术路径。