RAG-Based Language Model with Enhanced Indexing Optimization for Financial Intelligent Customer Service Application

Large language models face significant challenges in specialized domains where hallucinations are unacceptable under stringent accuracy requirements. This paper presents Retrieval-Augmented Generation Fusion (RAG-Fusion), a novel RAG system employing reciprocal rank fusion with multi-query generation to optimize knowledge retrieval and reduce hallucinations in financial customer service. Unlike traditional single-query RAG systems, our approach implements multi-query generation and Reciprocal Rank Fusion (RRF) based re-ranking mechanisms, integrating composite query construction and semantic vector weighting to improve recall of complex financial terminology. Through collaboration with China UnionPay Data (CUPD), we construct a comprehensive professional domain knowledge database addressing the shortage of specialized financial corpus. Experimental evaluation against ChatGPT-4 and experienced industry specialists demonstrates that our method achieves 86% accuracy compared to 45% of the baseline models, performs comparably to decade-experienced specialists while requiring only 22% of their response time, and excels at understanding deep semantics and open-ended questions in financial domain.