A Hybrid Retrieval-Generative AI Framework for FinTech Document Handling and Compliance Tracking

Abstract

Large language models (LLMs) have been quickly adopted in the financial services industry, allowing for sophisticated automation in document analysis, client service, compliance monitoring, and decision support. However, hallucinations, explainability issues, privacy concerns, and restricted access to valuable institutional information limit their use in regulated financial situations. Financial organizations need systems that ensure factual accuracy, traceability, and regulatory compliance in addition to producing fluid replies. This chapter describes an expert system for FinTech document intelligence based on Retrieval-Augmented Generation (RAG) that combines conventional term-based search with meaningful vector retrieval to guarantee dependable and auditable autonomous reasoning. The entire architecture, document ingesting pipeline, regulated prompt development, hybrid retrieval mechanism, variable structuring approaches, embedding generation, encryption model, and assessment methodology are all described. A thorough discussion is given of practical applications in financial services, identifying fraud, credit risk assessment, and adherence to regulations. A strategy plan for future study and establishment of policies is also presented, along with ethical issues and regulatory harmonization. Financial specialists can examine, confirm, and override artificially generated insights when needed thanks to the system's provision for human during validation. The platform facilitates regulatory examinations and improves transparency by keeping thorough derivation information and audit recordings for each generated answer. This method bridges the gap between the strict governance necessities of real-world economic ecosystems and advanced generative intelligence.