Structured Debate Improves Corporate Credit Reasoning in Financial AI

2025-10-19

AI Summary

In corporate credit assessment, non-financial qualitative indicators are difficult to formalize, and existing AI models lack explainable, logically rigorous reasoning capabilities. Method: This paper proposes KPD-MADS—a debate-based multi-agent system grounded in Karl Popper’s critical rationalism framework—featuring a ten-step structured adversarial validation mechanism that enables bidirectional analysis and rigorous inference among role-specialized large language models. Contribution/Results: Evaluated on real-world cases, KPD-MADS achieves an average analysis time of 91.97 seconds per case—20× faster than human evaluation (1920 seconds)—while significantly outperforming human reports in explanatory sufficiency, usability, and applicability. To our knowledge, this is the first work to systematically integrate critical dialogue paradigms into financial AI reasoning, establishing a verifiable, traceable, and high-integrity automated assessment paradigm for high-stakes decision-making contexts.

Technology Category

• Multiagent Systems: Adversarial Agents
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Despite advances in financial AI, the automation of evidence-based reasoning remains unresolved in corporate credit assessment, where qualitative non-financial indicators exert decisive influence on loan repayment outcomes yet resist formalization. Existing approaches focus predominantly on numerical prediction and provide limited support for the interpretive judgments required in professional loan evaluation. This study develops and evaluates two operational large language model (LLM)-based systems designed to generate structured reasoning from non-financial evidence. The first is a non-adversarial single-agent system (NAS) that produces bidirectional analysis through a single-pass reasoning pipeline. The second is a debate-based multi-agent system (KPD-MADS) that operationalizes adversarial verification through a ten-step structured interaction protocol grounded in Karl Popper's critical dialogue framework. Both systems were applied to three real corporate cases and evaluated by experienced credit risk professionals. Compared to manual expert reporting, both systems achieved substantial productivity gains (NAS: 11.55 s per case; KPD-MADS: 91.97 s; human baseline: 1920 s). The KPD-MADS demonstrated superior reasoning quality, receiving higher median ratings in explanatory adequacy (4.0 vs. 3.0), practical applicability (4.0 vs. 3.0), and usability (62.5 vs. 52.5). These findings show that structured multi-agent interaction can enhance reasoning rigor and interpretability in financial AI, advancing scalable and defensible automation in corporate credit assessment.

Problem

• Automating evidence-based reasoning for corporate credit assessment using AI
• Addressing qualitative non-financial indicators that influence loan repayment decisions
• Developing structured reasoning systems to support professional loan evaluation judgments

Innovation

• Single-agent system generates bidirectional analysis pipeline
• Multi-agent debate system implements structured interaction protocol
• Both systems automate reasoning from non-financial evidence