A Primer - AI Risk Management for Regulators and Supervisors

You sit across the table from firms whose models you did not build and cannot see inside. This primer is how to judge them anyway, in a few simple questions: one consistent approach, a net that catches AI as it enters, an inventory that things actually depend on, evidence tested to the firm's own task, controls that work as a system, and a deliberate decision about where fast is safe. It closes with the three places firms hide risk from you - the low rating, the inventory gap, and oversight on paper - and a start you can make on your next visit: ask for the full inventory, pick three systems rated low yourself, and ask why. Written by the person who led AI risk supervision at the Monetary Authority of Singapore and developed Singapore's AI risk management guidelines for the financial sector. Nothing in it depends on which instrument your regime anchors on. About twenty pages, written simply, and deliberately boring. This primer is the gateway to the full book, *AI Risk Management for Regulators and Supervisors* (listed here separately), which works each question in depth - what you are actually assessing, where firms hide risk, proportionality, the inventory and the rating, the controls before and after go-live, third-party AI, and capability on both sides of the table. If this primer was useful, pass it along.

For the supervisor's view and how this fits, see AI risk management for regulators and supervisors. https://quaintitative.com/ai-risk-management-for-supervisors/