Risk management teams in financial institutions produce and consume thousands of risk reports every month — exposure reports, stress test results, VaR reports, credit risk assessments, and regulatory capital calculations. These reports contain critical information about the institution's risk profile but are often stored as static PDFs in document repositories, making it difficult to compare across periods or aggregate insights. A RAG system over risk reports transforms this static reporting into an interactive risk intelligence platform.
The Risk Report Challenge
A bank's risk reporting infrastructure generates monthly reports across market risk, credit risk, operational risk, and liquidity risk. Each report contains valuable data, but the information is siloed across reporting periods and risk types. Risk analysts spend significant time manually cross-referencing reports to understand trends, exceptions, and emerging risks.
Risk Documents for RAG
- Market risk reports — VaR, stressed VaR, IR delta, CS01, and risk factor sensitivity reports
- Credit risk assessments — Counterparty exposure, PD/LGD/EAD metrics, collateral valuations, and watch list reports
- Stress test results — Regulatory stress test scenarios, internal stress test outcomes, and reverse stress test analyses
- Operational risk reports — Loss event databases, RCSA results, KRI dashboards, and issue management reports
- Regulatory capital calculations — RWA reports, capital adequacy ratios, leverage ratios, and CCAR/DFAST submissions
Architecture for Risk Report RAG
The recommended stack uses LangChain for flexible pipeline orchestration with OpenAI text-embedding-3-large for general-purpose embedding. Unstructured.io handles parsing of complex risk report PDFs that contain tables, charts, and regulatory footnotes.
Time-Series Retrieval
Tag each risk report chunk with reporting date metadata to enable retrieval of specific time periods. For trend analysis, retrieve multiple periods and synthesize with the LLM. A query like "How has our interest rate risk exposure changed over the last four quarters?" retrieves VaR reports from the relevant periods and generates a trend summary.
Risk Type Classification
Use metadata tagging to classify chunks by risk type — market, credit, operational, liquidity — and by report type — exposure, stress test, regulatory. This enables risk teams to query specific risk domains while filtering out irrelevant results. A credit risk analyst queries only credit risk reports without noise from market risk data.
Regulatory Risk Reporting
Build a RAG system over Basel, IFRS 9, and internal risk policy documents enabling risk teams to instantly verify regulatory compliance of risk calculations. When a new regulation is published, the RAG system can cross-reference existing risk reports to identify gaps and required adjustments.