Strategies for Enhancing Explainability in Financial Underwriting Using RAG and LLM

Recent advances in large language models (LLMs) have significantly expanded the capabilities of natural language processing systems. In the financial domain, delivering explainable and reliable personalised feedback has become increasingly important. However, traditional rule-based loan screening systems fall short in reflecting an individual’s financial context and providing actionable guidance. To address these limitations, this study proposes an LLM based loan screening system enhanced with RAG (retrieval augmented generation). The system retrieves relevant financial documents via vector-based search and feeds the context into a generative model. By leveraging external knowledge in real time, the system offers document-grounded, personalised financial recommendations. Experiments using real customer data show that the proposed RAG-based system significantly outperforms existing methods in both information retrieval and language generation. These results demonstrate the system’s potential to enhance the reliability and effectiveness of personalised financial services through the integration of professional document references.