Retrieval-Augmented Generation for Financial Intelligence: A Framework for Evidence-Based Decision-Making

Abstract

Financial decisions must rest on evidence: what a filing says, what a policy requires, what a contract permits, and what the market has reported. Large language models (LLMs) can read and synthesize such material quickly, but on their own they answer from memory, which may be outdated, incomplete, or invented. Retrieval-augmented generation (RAG) addresses this by retrieving relevant documents and grounding the model's output in them. In finance, however, grounding is not enough. The evidence must be current, authoritative, permitted for the user, numerically accurate, cited, and traceable into the decision it supports. This article proposes a framework for RAG-based financial intelligence built around evidence quality. It combines event-driven knowledge ingestion, an evidence-aware index, entitlement-aware and time-aware retrieval, deliberate context assembly, citation-bound generation guided by prompt patterns, a verification layer with guardrails and numerical recomputation, workflow integration through evidence packs, and continuous evaluation. Using a design-oriented approach grounded in ten studies published between 2020 and 2024, the article maps the evidence base and presents the framework and its evidence chain. It defines evidence quality criteria, failure modes with their controls, and evaluation metrics. It illustrates the framework with an asset manager's investment committee reviewing a holding after an earnings release and a credit downgrade. It argues that RAG supports sound financial decisions only when every claim can be traced to permitted, current, and verified evidence