When a regulator asks why an alert was closed six months ago, can your firm reconstruct the full decision? You need the data the model used, the logic and thresholds active at the time, the change and approval history, and the analyst’s rationale.
This ebook gives compliance and surveillance leaders a practical framework for making those decisions easier to explain and defend.
Why Explainability Matters
Surveillance programs now cover more venues, instruments, communications channels, and market activity. AI can help teams rank alerts, detect patterns, and review more data. It also adds inputs, model versions, outputs, and analyst actions to the decision record.
Weak data, closed detection logic, and incomplete governance history make that record difficult to reconstruct. The ebook explains how firms can preserve the evidence behind an alert while they tune models, reduce noise, and extend surveillance coverage.
What You Will Learn
- The four capabilities behind a defensible surveillance decision: detection logic you can inspect, complete evidence, governed change, and continuous improvement
- Where fragmented data, closed models, missing change history, and alert volume create gaps in the decision record
- How trusted data, broad coverage, configurable detection, governed workflows, and AI support contribute to modern surveillance
- What teams should record when AI influences alert scoring, triage, investigation, escalation, or closure
- How to test one historical alert and identify the evidence your firm can and cannot reconstruct
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Who Should Read This Ebook
This ebook is for chief compliance officers, heads of surveillance, and senior compliance leaders at banks, broker-dealers, and exchanges. It is especially relevant for teams reviewing alert quality, model governance, AI use, investigation workflows, or readiness for regulatory inquiry.
The Explainability Standard provides a framework for assessing each part of that record and finding the gaps that need attention.