Elevating Fairness in Consumer Credit Assessments: A Large Language Model (LLM) Driven Approach
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Credit assessments play a vital role in Government lending, Banking, and Financial Services, impacting access to credit for individuals and businesses. Traditional methods often suffer from limited data scope and fragmented task evaluation. To address these challenges, we developed the Large Language Model in Credit Assessment (LMiCA) system, leveraging LLMs’ generalization capabilities to enhance credit evaluations. Our unique benchmark includes 27,000 clustered samples and 81,000 tuning samples for in-depth bias analysis. Evaluations against state-of-the-art models reveal LLMs outperform traditional methods, promoting fairer, more comprehensive assessments. By reducing reliance on outdated criteria, LLMs can expand access to credit and ensure equity.
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