CreditXAI: A Multi-Agent System for Explainable Corporate Credit Rating
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908611890905088 |
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| author | Shi, Yumeng Yang, Zhongliang Wang, Yisi Zhou, Linna |
| author_facet | Shi, Yumeng Yang, Zhongliang Wang, Yisi Zhou, Linna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22222 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CreditXAI: A Multi-Agent System for Explainable Corporate Credit Rating Shi, Yumeng Yang, Zhongliang Wang, Yisi Zhou, Linna Multiagent Systems Computational Engineering, Finance, and Science 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. |
| title | CreditXAI: A Multi-Agent System for Explainable Corporate Credit Rating |
| topic | Multiagent Systems Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.22222 |