MASCA: LLM based-Multi Agents System for Credit Assessment

• URL: http://arxiv.org/abs/2507.22758v1
• Date: Wed, 30 Jul 2025 15:19:38 GMT
• Title: MASCA: LLM based-Multi Agents System for Credit Assessment
• Authors: Gautam Jajoo, Pranjal A Chitale, Saksham Agarwal,
• Abstract summary: We introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes.<n>We also present a signaling game theory perspective on hierarchical multi-agent systems, offering theoretical insights into their structure and interactions.
• Score: 0.3277163122167434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment remains an underexplored challenge, traditionally dependent on rule-based methods and statistical models. In this paper, we introduce MASCA, an LLM-driven multi-agent system designed to enhance credit evaluation by mirroring real-world decision-making processes. The framework employs a layered architecture where specialized LLM-based agents collaboratively tackle sub-tasks. Additionally, we integrate contrastive learning for risk and reward assessment to optimize decision-making. We further present a signaling game theory perspective on hierarchical multi-agent systems, offering theoretical insights into their structure and interactions. Our paper also includes a detailed bias analysis in credit assessment, addressing fairness concerns. Experimental results demonstrate that MASCA outperforms baseline approaches, highlighting the effectiveness of hierarchical LLM-based multi-agent systems in financial applications, particularly in credit scoring.

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