Beyond the credit score: The untapped power of LLMS in banking risk models

Abstract

Traditional credit scoring models have long been the cornerstone of risk assessment in banking. However, these models often rely on limited, structured data and fail to capture the nuanced behavioral and contextual signals embedded in unstructured information. This review explores the transformative potential of large language models (LLMs) in enhancing banking risk models beyond conventional credit scoring. By leveraging their advanced natural language processing capabilities, LLMs can analyze diverse sources such as transaction narratives, customer communications, social media sentiment, and financial news to extract deeper insights into borrower behavior and creditworthiness. The paper examines how LLMs can improve risk prediction accuracy, enable more inclusive credit assessments, and uncover latent risk factors, particularly in underbanked populations. It also discusses the technical, ethical, and regulatory challenges of integrating LLMs into financial systems, including model interpretability, bias mitigation, and compliance with data privacy laws. Through a comprehensive synthesis of current research, emerging use cases, and industry developments, this review highlights the untapped potential of LLMs to redefine risk modeling in the modern banking landscape. Keywords: AI Compliance, Regulatory Challenges, Model Development, Implementation Strategies, Risk Management.