CreditXAI: A Multi-Agent System for Explainable Corporate Credit Rating

2025-10-25

AI Summary

Deep learning models for corporate credit rating suffer from poor interpretability and lack hierarchical reasoning mechanisms. Method: This paper proposes a multi-agent collaborative hierarchical reasoning framework that emulates domain experts’ division of labor across business, financial, and governance risk dimensions. It jointly models financial and non-financial information to enable traceable and comprehensible credit assessment. The framework enhances transparency and consistency through risk-dimension decomposition, task-oriented agent assignment, and cross-agent consensus reasoning. Contribution/Results: Experiments demonstrate that the proposed method improves prediction accuracy by over 7% compared to the best single-agent baseline, while significantly enhancing model interpretability and holistic analytical capability. It establishes a new paradigm for corporate credit risk assessment that balances high performance with decision trustworthiness.

Technology Category

• Multiagent Systems: Modeling other Agents
• Knowledge Representation and Reasoning: Reasoning with Beliefs
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Addresses the black-box problem in corporate credit rating models
• Enhances interpretability through hierarchical reasoning mechanisms
• Improves predictive accuracy using multi-agent collaborative decision-making

Innovation

• Multi-agent system simulates analyst decision-making process
• Focuses on business, financial, and governance risk dimensions
• Improves predictive accuracy through collaborative agent framework