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.