A Multi-Hop Retrieval-Augmented Generation Framework for Intelligent Document Question Answering in Financial and Compliance Contexts

We present FinLLaMA-RAG, a Retrieval-Augmented Generation (RAG) framework built on LLaMA 3 to address complex document-level question answering. The model integrates dense retrieval, contextual fusion, and multi-hop reasoning to extract accurate, coherent answers from long and cross-referenced documents. Designed with high-stakes applications in mind, FinLLaMA-RAG supports automated tax compliance, financial fraud investigation, and regulatory reporting. Through joint optimization of retrieval and generation, it achieves strong results across several financial QA datasets. This work contributes to the development of explainable and reliable AI tools for financial and policy-oriented document analysis.