Beyond the AI Claim: What Should Financial Institutions Ask Their RegTech Vendors?

Nearly every RegTech vendor now claims some version of “AI-powered” technology For a compliance function deciding who to trust with tax documentation, onboarding data, and reporting obligations, that claim on its own says very little. It doesn’t say what the AI actually does, where a human is still involved, or how a decision it makes could be traced back and explained if a regulator asked. 

As AI becomes a standard feature of RegTech marketing, the more useful question for a financial institution isn’t “does this vendor use AI”, it’s “what evidence backs that claim up, and what happens when the AI gets something wrong.” 

Why the claim alone isn’t enough 

An AI-supported process is only as trustworthy as what stands behind it: the data it was built on, the scope of what it’s actually applied to, and the checks that catch it when it’s uncertain or incorrect. Two vendors can both accurately say “we use AI,” and mean two very different things — one might mean a narrow, well-scoped model applied to a specific, well-understood problem with human review built in; the other might mean a broad claim with little visibility into where the model is actually making decisions versus assisting a human who still makes the final call. 

For a compliance function, that distinction isn’t a technical footnote. It’s the difference between being able to explain a decision to a regulator and not. Being able to rely on it or not. A model that quietly makes a judgment call with no visible flag, versus one that surfaces uncertainty and routes it to a person — both might be described as “AI-powered” in a sales deck, but only one of them produces something a compliance officer can stand behind under questioning. 

Questions worth asking before you trust the claim 

What specifically is the AI doing? Vague answers here are a warning sign. A concrete answer looks like an AI-supported OCR model, purpose-built to extract, normalize, and validate data from CRS self-certification forms regardless of layout or formatting — a specific, bounded task rather than an open-ended “AI reviews your compliance.” Similarly, AI applied to ingest non-standard withholding statements — accepting whatever format an intermediary provides and building a structured version automatically, is a defined problem with a defined scope, not a general claim. If a vendor can’t name the specific task the model performs, that’s usually a sign the claim is more marketing than architecture. 

What data grounds its decisions? Ask what the model is actually validating against. Is it checking extracted data against a defined standard, OECD CRS requirements, IRS TIN formats, jurisdiction-specific rules, or is it making judgment calls without a clear reference point? Validation against a known, documented standard is verifiable; you can ask to see the rule that was applied. A black-box judgment isn’t, and there’s no way to audit a decision that was made against criteria nobody can point to. 

Where does a human still make the call? No responsible use of AI in tax compliance removes human oversight entirely. The better question is where oversight sits: does an automated model flag a form for review when data can’t be confidently extracted, or does it silently guess? A defensible process makes ambiguity visible, for instance, surfacing blank fields clearly for quick human resolution rather than filling gaps with an unverified assumption. Ask specifically: what does the system do in the 5% of cases that don’t fit the pattern cleanly? That answer tells you more than any claim about accuracy in the other 95%. 

How are decisions logged and how would you prove it later? If a regulator asks why an account was treated a certain way, can the vendor show you exactly what happened, what was validated, when, against what rule, and what the outcome was? An audit-ready compliance trail, capturing every validation and submission with full traceability, is what turns “trust us” into something you can actually demonstrate. This matters just as much for the AI-driven parts of the process as it does for the rest, an audit trail that stops at “the model decided” without recording the basis for that decision isn’t really an audit trail at all. 

What happens when the AI is wrong? Every model has an error rate. The relevant question isn’t whether errors happen, but what the system does when they do, does it flag uncertainty, route to a reviewer, and leave a clear record, or does an error propagate silently into a downstream report? Ask for an example of a real case where the model got something wrong and what the recovery process looked like. A vendor with a mature process will have a ready answer; one without will struggle to describe anything beyond “it’s very accurate.” 

How was the model scoped and tested, and does that scope stay fixed? An AI capability that was purpose-built for one narrow task, say, CRS form OCR, and rigorously tested within that boundary is a very different proposition from a general-purpose model quietly expanded to cover new tasks without the same testing rigor applied each time. Ask whether the AI’s scope has grown since it was first deployed, and if so, what validation process accompanied that expansion. 

The shift in what “trustworthy AI” means 

Financial institutions are increasingly expected to demonstrate not just what their compliance processes do, but how confident they can be in them and why. That expectation is starting to extend to the vendors institutions rely on. Internal model risk management frameworks that have long applied to credit and fraud models are increasingly being asked, informally if not yet formally, to extend to compliance and tax-documentation AI too. A vendor that can answer these questions specifically, with named capabilities, defined scope, and a visible audit trail, is offering something fundamentally different from one that answers only in general terms about “AI-driven compliance.” 

Making this part of procurement, not just IT due diligence 

These questions don’t need to sit solely with a technology or security team during vendor onboarding. They’re compliance questions as much as technical ones, and the answers are things a compliance officer should be comfortable explaining upward, to their own leadership, and eventually to a regulator, in plain terms. If the answers only make sense to an engineer, that’s worth treating as a gap in itself, not just a communication issue. 

The claim that a RegTech platform “uses AI” should be the start of the due diligence conversation, not the end of it. What actually matters is whether that AI is scoped, grounded, auditable, and built with a human still accountable for the outcome. 

TAINA’s AI capabilities, including AI-supported OCR for CRS self-certification validation and AI-driven ingestion of non-standard withholding statements, are built with defined scope, human review points, and a full audit trail behind every decision. Get in touch at [email protected] or visit www.taina.tech to learn more. 

If you’d like to see how TAINA can simplify and streamline your CARF and CRS compliance journey, we’d be delighted to request a demo.

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