RAG-Enhanced Evidence Recommendation in Financial Legal Resolutions

The complexity of legal documents, particularly in financial legal disputes, poses significant challenges for both experts and automated systems. This study introduces a system leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology to recommend key evidence in financial advisor dispute cases. Unlike traditional legal AI tasks focused on outcome prediction, our approach emphasizes supporting judicial reasoning by recommending key evidence relevant to adjudication. We constructed a dataset of 371 annotated cases from Taiwan, spanning 25 years, including claims, judicial opinions, and final judgments, with annotations highlighting key evidence. Using RAG, our system retrieves and generates evidence recommendations grounded in analogous past cases while maintaining temporal and contextual consistency. This methodology enhances judicial efficiency and supports equitable legal decision-making by streamlining the recommendation of critical evidence.