@article{Chai2026, 
author = {Hongfeng Chai and Renfeng Chen and Hongbin Zhu},
title = {RAG-Based Language Model with Enhanced Indexing Optimization for Financial Intelligent Customer Service Application},
year = {2026},
journal = {Big Data Mining and Analytics},
volume = {9},
number = {4},
pages = {1046-1060},
keywords = {Retrieval-Augmented Generation (RAG), enhanced indexing, Reciprocal Rank Fusion (RRF), augmented knowledge database, financial intelligent customer service},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020111},
doi = {10.26599/BDMA.2025.9020111},
abstract = {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.}
}