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Agent Architecture Design and Tooling

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Linked challenge: Build an Explainable AML Agent with Claude & Gemini for Enhanced Sanctions Screening

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Linked challenge

Build an Explainable AML Agent with Claude & Gemini for Enhanced Sanctions Screening

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Design the overall architecture for your AML agent using `Claude Agents SDK`. Detail how it will ingest simulated transaction data, perform entity resolution, and leverage `Gemini 2.5 Flash` for risk scoring and sanctions screening. Outline the specific tools the agent will need (e.g., `TransactionDataIngestorTool`, `EntityResolverTool`, `SanctionsCheckerTool`, `RiskScoringTool`) and describe their functionalities and required inputs/outputs. Consider the sequence of operations and how the `Claude Agents SDK` will orchestrate these tools.

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Linked challenge

Build an Explainable AML Agent with Claude & Gemini for Enhanced Sanctions Screening

Financial institutions face increasing pressure to combat money laundering and terrorist financing, intensified by regulatory developments like DORA, calls for AI control by central banks, and the evolving landscape of beneficial ownership information. The demand for highly efficient, transparent, and compliant Anti-Money Laundering (AML) systems is paramount. This challenge focuses on leveraging cutting-edge AI agent technology to autonomously identify and investigate suspicious financial activities, addressing the gaps left by traditional rule-based systems and the need for explainability. Participants will utilize the `Claude Agents SDK` to orchestrate a sophisticated AML agent. This agent will ingest simulated transaction data, perform robust entity resolution to link disparate financial entities, and interact with the `Gemini 2.5 Flash` model for advanced risk scoring, anomaly detection, and real-time sanctions screening. A critical component will be the generation of clear, auditable explanations for every alert, satisfying regulatory demands for transparency and aiding human investigators in efficient triage. The solution must also incorporate secure data handling practices, reflecting DORA compliance principles for cryptographic migrations and data integrity in multi-cloud environments. This practical challenge aims to develop an intelligent system that not only detects financial crime but also provides clear, traceable evidence and reasoning behind its decisions, significantly reducing false positives and enhancing the overall effectiveness of AML operations in a complex regulatory landscape.

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