Shubhradeep Nandi

Credit assessments are pivotal in Government lending, Banking, and Financial Services, shaping credit availability and terms for individuals and businesses. Traditional methods face limitations like narrow knowledge and segmented tasks. This study explores the potential of Large Language Models (LLMs) in credit assessments, presenting a comprehensive framework. Our benchmark includes 27,000 clustered datasets and 81,000 tuning samples tailored for credit evaluation, with an in-depth analysis of LLM biases. Introducing LMiCA, our novel system integrates LLMs to interpret diverse credit data effectively. Evaluations against state-of-the-art methods and open LLMs highlight their superior performance, promising more equitable and accurate credit assessments, potentially broadening financial access. Keywords—Credit Assessments, Large Language Models