BankCARE: Advancing Bank Services with Enhanced LLM and Retrieval Generation

In the context of coping with the rapid development of fintech and the transformation of banking online,
this study explores a retrieval augmented generation RAG -
based approach aiming to enhance the efficiency and quality
of bank customer service. Considering the challenges faced by
existing bank customer service systems in handling complex
requirements and personalised solutions, especially data privacy
protection and dialogue system intelligence, this paper proposes
a novel LangChain-based RAG framework. This framework
performs vector indexing and similarity search by integrating
FAISS, and employs multiple embedding models for data processing and chunking to accurately and efficiently capture and
respond to customer needs. By processing external information
in real-time, this method is able to adjust the response to
the specific context of the query, improving the accuracy and
adaptability of the response. The validation on a real dataset
of bank customer service demonstrates the advantages of this
research method over existing techniques in improving response
speed and quality, significantly enhancing customer satisfaction
and contributing to the development of the FinTech field.