arXiv:2507.22758v2 Announce Type: replace-cross 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
Advances in large language models and agent-based systems are increasingly being applied to practical financial problems beyond traditional areas like trading.
This development indicates a maturation of AI agents for white-collar tasks, moving beyond theoretical applications to automate complex decision-making processes like credit assessment.
Credit assessment, traditionally reliant on statistical models and human judgment, can now be significantly augmented or even replaced by collaborative AI multi-agent systems, implying greater efficiency and potentially altered risk profiles.
- · AI software providers
- · Financial institutions adopting AI
- · Fintech companies
- · Legacy credit assessment firms
- · Traditional credit analysts
- · Rule-based financial modeling platforms
Enhanced efficiency and consistency in credit evaluation across financial institutions.
Increased competition among lenders due to standardized, AI-driven risk assessment and faster loan processing.
Potential for new credit products and altered market dynamics as AI identifies previously overlooked credit opportunities or risks.
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Read at arXiv cs.LG