A subtle but significant change is reshaping how regulated firms and their supervisors interact, and many risk and compliance teams have yet to fully absorb its implications.

According to Sherlocq, for around 20 years, supervisory oversight followed a predictable pattern. Firms controlled the data, and when regulators requested information, firms decided how it was packaged, submitted and explained.

Supervisors relied heavily on whatever firms chose to share, and experienced compliance professionals knew that inspection readiness was as much about presentation as substance.

That model is now unravelling. Supervisors can analyse board minutes, management reports, policies, procedures and large data sets with unprecedented speed. They can summarise complex policies, test them against regulatory requirements and interrogate vast volumes of data. Work that once took weeks to produce initial findings can now be completed on the first day of an inspection.

Regulators across financial stability, data protection, consumer markets and other areas have invested heavily in data infrastructure. The UK’s Financial Conduct Authority has expanded its data science capability to track transaction patterns, identify outliers and model firm behaviour at scale.

The European Central Bank’s supervisory arm deploys automated tools to cross-check disclosures for inconsistencies. In the US, the SEC’s enforcement division increasingly uses analytics to spot potential misconduct before formal investigations begin, while every regulator in the UAE is developing SupTech capabilities, often incorporating artificial intelligence.

The upshot is that supervisors increasingly enter conversations with a view already formed, built on what the data revealed independently. A regulator may know that complaints rose months before a firm raised a product issue, or that its conduct metrics place it well outside peer norms.

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