Authors - P. Pandiaraja, P.Krishna Kishore, E. Ganesh, C. Selvarathi, Charles Prabu V, S. Jagan Abstract - Large Language Models have facilitated the development of sophist i-cated smart platforms that are actively leveraged in the provision of financialservices to various classes of customers. This advancement has enabled peopleto obtain individual financial advice. This paper presents a framework for buil d-ing a financial chatbot that incorporates Retrieval Augmented Generation(RAG) technology and several SQL agents to improve reliability. The proposedapproach addresses five fundamental challenges in financial artificial inte ll igence: eradicating hallucinations, obtaining up to date information, utilising u s-er facts to tailor individual suggestions, safeguarding user privacy, and provi d-ing clear explanations. RAG is used to retrieve verified financial knowledge,while SQL agen ts query databases to produce accurate outputs. The solutionprovides advisory responses that are relevant to users and protect sensitive i n-formation through a zero trust security architecture. The system architecture i n-corporates multiple validation check points and is dynamically configured tomeet individual user requirements. Experimental results demonstrate a 96.2%accuracy rate in handling financial queries with a 3.8% error rate and a mean r e-sponse time of 1.5 seconds, outperforming comparable solutio ns. The proposedarchitecture establishes a reliable baseline for financial professionals seekingdependable advisory services.
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