Agent-Orchestrated Scenario Analysis for Fraud and Compliance

Added Nov 2025 3 design docs

When a bank confronts a new situation, such as a suspected fraud pattern or an unfamiliar compliance question, experienced staff instinctively recall similar past cases and what was done about them. Encoding that instinct in software, so any analyst can ask what a scenario resembles and what actions are advisable, is the goal of this project. The intern builds a python application where users describe or upload hypothetical banking scenarios covering areas like fraud detection and compliance checks. A langgraph workflow structures the analysis: the scenario is parsed, matched against a library of historical cases, and passed to a generation stage where openai models produce recommended actions and rationale. An agentic-ai layer coordinates the matching and generation steps, deciding when a scenario needs clarification or a second comparison pass. The output pairs each input scenario with its closest precedents and a clear, structured set of suggested next steps. Through this build the intern learns scenario analysis, similarity-based reasoning with generative AI, and modular workflow design, and gains a concrete story about how agent-style orchestration supports risk decisions in banking.

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