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

Yumeng Shi, Zhongliang Yang, Yisi Wang, Linna Zhou

TL;DR

CreditXAI tackles the interpretability and structured reasoning gap in corporate credit rating by introducing a four-layer multi-agent system that mirrors a professional analyst team. The Analysis Layer houses BRA, FRA, CRA, and GRA, which generate risk signals from multimodal data, with the Decision Layer (CAA and RRA) producing a final, explainable rating and report; a Supervisory Layer (SSA) ensures traceability. Empirical results show that hierarchical fusion (CRA and CAA) yields up to 7.9% higher accuracy than the best single agent, with substantial gains from historical learning across agents. The framework offers a scalable, transparent pathway to intelligent credit risk assessment, while acknowledging limitations and opportunities to incorporate broader data sources and real-world deployment considerations.

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.

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

TL;DR

CreditXAI tackles the interpretability and structured reasoning gap in corporate credit rating by introducing a four-layer multi-agent system that mirrors a professional analyst team. The Analysis Layer houses BRA, FRA, CRA, and GRA, which generate risk signals from multimodal data, with the Decision Layer (CAA and RRA) producing a final, explainable rating and report; a Supervisory Layer (SSA) ensures traceability. Empirical results show that hierarchical fusion (CRA and CAA) yields up to 7.9% higher accuracy than the best single agent, with substantial gains from historical learning across agents. The framework offers a scalable, transparent pathway to intelligent credit risk assessment, while acknowledging limitations and opportunities to incorporate broader data sources and real-world deployment considerations.

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.
Paper Structure (25 sections, 9 equations, 2 figures, 5 tables)

This paper contains 25 sections, 9 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Method
  4. System Architecture
  5. Data Processing Layer
  6. Financial Data Processing.
  7. 10-K Report Data Processing.
  8. Analysis Layer
  9. Business Risk Analysis Agent (BRA).
  10. Financial Risk Analysis Agent (FRA).
  11. Comprehensive Rating Agent (CRA).
  12. Governance Risk Analysis Agent (GRA).
  13. Decision Layer
  14. Chief Analyst Agent (CAA).
  15. Rating Report Writing Agent (RRA).
  16. ...and 10 more sections

Figures (2)

  • Figure 1: Overview of the CreditXAI framework. This multi-agent system is organized into four layers of specialized agents: the Analytical Layer (BRA, FRA, GRA, CRA), the Decision Layer (CAA, RRA), and the Supervisory Layer (SSA), all collaborating to generate explainable corporate credit ratings.
  • Figure 2: Overview of the CreditXAI framework.