RAG-Powered LLM Risk-Management Agent
Grounded LLM Reasoning for Financial Risk Analysis
A proof of concept for retrieval-augmented generation in financial risk analysis, grounding LLM reasoning in live market data and news to deliver cited, explainable risk insights.
- Type
- Cognifinity R&D PoC
- Domain
- Financial risk analysis
- Focus
- Grounded LLM reasoning
Problem
Financial risk analysts need to make sense of rapid price movements in the context of breaking news, earnings reports, and market sentiment, all in real time. Traditional dashboards surface raw data but cannot explain why a move is happening or what the relevant context is.
Large language models can reason over text fluently, but their outputs are prone to hallucination and cannot be trusted without grounding in verifiable, up-to-date sources.
Goal: An agent that combines live market data with relevant news, answers natural-language risk queries with cited evidence, and proactively alerts analysts to significant price events.
Approach
The system is built as four coordinated micro-services deployed via Docker Compose, validated on a live market use case (NVDA equity price and news).
System Architecture
- Ingestion Service: continuously polls live equity prices and news headlines, persisting them for downstream retrieval.
- Vector Store Service: embeds ingested news into a semantic vector store, enabling retrieval by meaning rather than keyword matching.
- LLM Service: on query, retrieves the most relevant news context and passes it to the language model, which reasons over the evidence and cites sources in its response.
- Dashboard & Alert Service: live price dashboard with an "Explain move" button that surfaces cited LLM reasoning, plus automated alerts on significant price moves.
RAG Pipeline
Queries flow through four stages: a Query Builder structures the user request, a Retriever surfaces semantically relevant news chunks from the vector store, an LLM Reasoner synthesises an answer grounded in those chunks, and a Self-Check layer validates citations and numeric claims before returning the response.
The broader architecture supports portfolio-scale deployment: Data Sources (market feeds, positions, news, filings) flow through the Embedding Service into the Vector DB, which feeds the Retriever and LLM Risk-Management Agent, with outputs reaching Dashboards & Risk Alerts and a Human Risk Officer feedback loop.
Explore RAG for Financial Intelligence?
Get in touch to discuss how retrieval-augmented LLMs can ground risk analysis in verified, real-time evidence.
Contact R&D Team