DERIV
SignalSAR is an AI-powered compliance copilot for transaction monitoring and suspicious activity reporting. We built it to solve a core AML pain point: too many low-value alerts and too much manual SAR writing. SignalSAR combines rule logic, behavioral anomaly signals, and network linkage analysis (shared IP/device/card patterns) to prioritize high-confidence cases. Analysts receive an investigation-ready case pack with timeline, reason codes, and evidence context, then review an auto-generated SAR draft with compliance completeness checks. The workflow supports fast human-in-the-loop decisions, including intervention actions and full audit trails. In our demo, SignalSAR shows how teams can move from high-volume alert noise toward actionable cases, improve analyst productivity, and produce regulator-ready SAR narratives with minimal edits using synthetic data.
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