Enhancing Financial Risk Management with Retrieval-Augmented Large Language Models

With the rapid growth of financial markets, traditional credit scoring and fraud detection models face significant challenges, such as insufficient historical data and poor adaptability. This paper proposes a novel financial risk management framework by integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques. By dynamically incorporating unstructured information like financial news and social media content, the proposed approach effectively enhances prediction accuracy and real-time responsiveness. Experiments using the Lending Club dataset demonstrate that the RAG-enhanced model significantly outperforms traditional methods, achieving notable improvements across various metrics, thus highlighting its potential value for financial institutions.