PRAXANAEvery monitoring vendor now leads with machine learning. Some of it is transformative; some of it is a logistic regression in a trench coat. Compliance officers do not need to become data scientists, but they do need a working evaluation framework.
Questions that separate substance from marketing
What data does the model actually use, and do we lawfully hold it at the quality required? What happens when the model is wrong in each direction β who reviews false negatives, and what workload do false positives create? Can the vendor explain, for a specific alert, why it fired, in language an analyst and a regulator can follow? If the answer to the last question is 'the model is proprietary', price in the supervisory conversation you will eventually have.
Governance is the real requirement
Regulators have been consistent: they are not against advanced analytics, they are against unmanaged ones. That means documented model validation before go-live, ongoing performance monitoring with defined triggers for revalidation, clear human accountability for model outcomes, and change control when the model or its inputs shift. If your organisation has a model risk management framework for credit models, financial crime models belong inside it.
Run models in parallel first
The safest adoption path is unglamorous: run the new model alongside the existing rules for a full cycle, compare what each catches and misses, and only then decide what the model replaces versus supplements. Parallel runs surface the uncomfortable finding vendors rarely volunteer β models are often excellent at re-ranking known risk and less proven at finding genuinely novel typologies.
Keep the narrative honest
Internally and externally, describe what the system does in plain language. Overselling AI capability to a board or regulator creates an expectations gap that surfaces during the first missed case.