This article is provided for general information and does not constitute legal or regulatory advice. It reflects publicly available sources as at the date of publication. Regulatory expectations and timelines change; confirm your own position with your compliance function and the relevant authority.
RegTech is not a synonym for AI, and treating the two as interchangeable is expensive in a specific way: it sends teams shopping for a model when the workflow in front of them is still undocumented.
Most of the compliance estate inside a bank or a licensed fintech is deterministic and unglamorous. It validates a field, enforces a threshold, screens a record against a list, holds a case open until the required document arrives, preserves evidence, and refuses an action nobody approved. It works precisely because it is boring: the condition was written down in advance, so the behavior is repeatable and a reviewer can explain it in one sentence twelve months later.
A model is useful somewhere else entirely - where the system has to read language, notice a pattern nobody wrote a rule for, or rank a queue that is longer than the team reviewing it. The two are not in competition, and the interesting engineering question is not "should we add AI to compliance". It is where the line between them falls, and what the system has to produce on either side of it.
For a fintech building that compliance layer rather than inheriting it, our guide to fintech software development covers scope, compliance and the delivery model.
One question does most of the work: can the condition be written down before the case arrives?
If it can, the control belongs in a rule. Rules are cheap to test, cheap to re-run, and they behave identically for the ten-thousandth case and the first. If it cannot - because the input is a letter, a contract, a chat log or a pattern that only looks wrong next to five other cases - then the work is interpretation, and interpretation is where a model has something to offer.
Answering it properly means walking the decision path once, end to end, before any technology decision is taken. Six prompts are usually enough to expose whether the workflow is ready for automation of any kind:
What triggers the review, and how consistently?
Which data is required, and who owns each source?
Which rules are explicit and already written down somewhere?
Where does human judgement genuinely enter, and who holds it?
What evidence must be retained, and for how long?
What happens when the system is uncertain?
There is a useful side effect to asking this honestly. Teams usually discover that a good share of what they call judgement is actually an undocumented rule: a threshold an experienced analyst applies from memory, a document everyone knows is mandatory, an escalation that always happens above a certain amount. Writing those down converts them into deterministic controls that are faster and more defensible than any model - a saving delivered before anyone has selected a vendor. The residue that survives the exercise is the genuine interpretation problem, and it is smaller and better defined than it looked at the start.
None of the following wants a model. Each wants a rule with a version and an effective date:
Required-document and completeness checks;
Transaction and exposure limits;
Access control and segregation of duties;
Retention and deletion schedules;
Approval chains and four-eyes checks; and
Record preservation on a case under review.
The reason to be firm about this is not conservatism. Deterministic controls are what make the model-assisted parts of the service deployable at all, because they form the boundary the model cannot cross. A system may summarize a case and point an analyst at the three paragraphs that matter; the deterministic layer still decides who is allowed to approve the outcome, which records must exist before the case can close, and whether it may proceed. Remove that boundary and every question about the model becomes a question about the whole institution's control environment.