Risk-Aware Financial RAG with Distributional Retrieval Policies

Financial retrieval-augmented generation (RAG) systems are increasingly used to summarize market events, explain portfolio exposures, answer policy questions, and support regulated advisory workflows. Standard retrieval pipelines optimize semantic relevance and latency, but financial decisions are shaped by asymmetric losses, tail events, privacy constraints, stale evidence, and persuasive narrative drift. This paper proposes Risk-Aware Financial RAG (RAF-RAG), a synthetic architecture in which a retriever is constrained by distributional risk budgets, governed predicates, anonymized evidence packs, multi-agent contracts, and long-horizon market indicators. RAF-RAG extends Retrieval-Grounded Documentation Agents for Enterprise Compliance Evidence by requiring evidence-supported financial claims, extends Cross-Cloud LLMOps Scheduler for Privacy-Budgeted RAG and Inference by routing retrieval and inference under privacy and endpoint budgets, and extends Ideological Drift Detection in Governed Enterprise Knowledge Bases by detecting persuasive drift in financial narratives before it changes recommendations. We define a conditional-value-at-risk retrieval objective, a distributional policy loop, and a simulated benchmark over portfolio review, market commentary, and compliance-question workloads. In simulation, RAF-RAG improves tail-risk violation detection F1 from 0.62 to 0.85, reduces unsupported financial claim rate by 52.4%, and preserves zero unauthorized privacy-budget overruns.