Risk Scenario Mapping Tool with Case and Regulation Retrieval

Added Nov 2025 3 design docs

Risk officers evaluating a hypothetical, such as a new fraud vector or a potential compliance breach, reason by reference: they look for similar past cases, check which regulations apply, and only then recommend action. This project encodes that reference-driven method into a tool. The intern builds it in python with a streamlit interface. A knowledge base of historical risk cases and relevant regulatory summaries is embedded into a vector database. A user describes or uploads a hypothetical banking risk scenario, and a retrieval-augmented generation pipeline maps it to the most similar historical cases and the regulations it touches. Generative AI then composes an assessment grounded in the retrieved material: how comparable situations unfolded, which rules are implicated, and a set of recommended actions with reasoning. The interface presents scenario, precedents, regulations, and recommendations together, so the chain of reasoning is inspectable end to end. By building the tool the intern learns risk analysis methodology, similarity retrieval across heterogeneous sources, and grounded recommendation generation with RAG, a directly marketable combination in banking technology.

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