Insights
Compliance, financial crime, and what AI really changes.
Regulatory developments translated into operational consequences, and honest assessments of where machine intelligence belongs in a control environment.
What AI actually fixes in transaction monitoring — and what it doesn't
Machine learning is very good at ranking alerts and very bad at inventing typologies nobody has described. A practitioner's map of where the lift is real, where it is marketing, and what has to be in place before either.
The EU AML package: what actually lands on operations teams
Beyond the headline of a single rulebook and a new authority — the specific changes to beneficial ownership, cash limits and supervisory expectations that will reshape day-to-day control design.
Measuring an analyst, not their output
Alert throughput is a terrible quality metric. A weighted QA framework for scoring decision quality — narrative, escalation judgement, evidence handling — without turning review into a productivity race.
Manufactured volume: detecting incentive abuse on platforms
When a platform rewards growth, some of that growth gets manufactured. How to build a screen that distinguishes real traction from self-funded activity — and how to raise it without accusing anyone.
Screening fuzziness is a policy decision — treat it like one
Match thresholds are usually inherited from a vendor default and never revisited. What a defensible calibration exercise looks like, and how to document the risk appetite behind it.
The model documentation a supervisor will actually ask for
Performance metrics are the easy part. The harder questions are about lineage, drift, challenger models and who signed off — a checklist for teams deploying their first model in a control.
The Compliance Signal
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