AI for RegTech Companies
A RegTech vendor carries an unusual burden: your product has to work, and your customers have to be able to prove to their regulator that it works, which is a different and harder requirement.
RegTech is the one vertical where explainability is not a constraint on the product but the product itself, because your customer is buying the ability to demonstrate something to a supervisor.
AI for RegTech covers regulatory change monitoring and obligation mapping, AML and sanctions screening false positive reduction, transaction monitoring, trade and communications surveillance, and regulatory reporting automation — built so that regulated customers can evidence it.
Your customer's validation requirement is your product requirement
A bank buying your screening model is accountable for it as though they built it, and the revised interagency model risk guidance effective April 2026 is explicit that vendor models require validation even where the customer cannot see inside them. What you supply for that validation determines whether you get through procurement.
| What customers need | What weak vendors supply | What actually works |
|---|---|---|
| Model documentation | A product brochure | Assumptions, lineage, limitations, methodology |
| Performance evidence | Aggregate accuracy | Recall and precision on populations like theirs |
| Explainability | A confidence score | Why this alert, in terms an investigator can write up |
| Recall assurance | Alert reduction percentage | Evidence that true positives were not suppressed |
| Version control | Continuous improvement | Pinned versions with change notification |
| Testing on their data | A reference customer | Shadow running on their alert history |
Alert reduction without recall evidence is not a sellable claim
Any model can cut alert volume by eighty per cent; the question is what went with it. A compliance officer cannot accept a reduction they cannot evidence preserved true positives, because the failure mode is a missed suspicious activity report and the consequence is personal. Build recall measurement into the product — sampling of suppressed alerts, back-testing against known outcomes — and lead with it. Vendors who lead with the reduction figure alone lose to vendors who lead with the recall evidence.
The AML evidence gap, which is a market opportunity
Over half of banks report false positive rates above twenty per cent, a quarter above forty, and more than half spend an hour or more investigating each alert. The problem is enormous and well documented. What is thin is published evidence that machine learning has measurably fixed it — industry surveys largely capture what institutions expect AI to deliver rather than what it has delivered.
That gap is your differentiator if you close it
A vendor who can show measured before-and-after on real customer alert populations, with recall preserved and the methodology open to inspection, is selling something almost nobody else is. This is a product decision more than a modelling one.
Suppression needs a defensible mechanism
Auto-closing alerts is a supervisory conversation. Ranking, batching and enriching them is a far easier sell and captures most of the investigator time saving without the exposure. Start there.
Investigation outcomes are the training signal nobody has
What investigators concluded, and why, is the label that makes alert models work. Most institutions capture disposition codes and discard reasoning. A product that captures reasoning well produces a compounding data advantage.
Screening is a different problem from monitoring
Name and entity matching against sanctions and PEP lists is a matching problem with transliteration, cultural naming and corporate structure complexity. It is more tractable than behavioural monitoring and the false positive burden is frequently larger. See semantic search.
Regulatory change monitoring, done properly
- Mapping is the value, not retrieval. Telling a customer a rule changed is commodity. Telling them which of their controls, policies and systems it touches is the product.
- Jurisdictional scope has to be explicit. A change relevant in one state and not another is the distinction customers pay for, and getting it wrong is worse than saying nothing.
- Recency and effective dates are structured data. Consultation, final rule, effective date and transitional provisions are different things and must never be conflated.
- Citations are mandatory. Every assertion traceable to the source text, because a compliance officer will check and one hallucinated obligation ends the relationship.
- Confident wrongness is the existential risk. A missed change is a gap; an invented obligation costs a customer money and destroys trust permanently.
How an engagement runs
Built so your customers can evidence it, because that is what they are actually buying.
Customer evidence requirements
What your regulated customers must demonstrate to their supervisors, and what that requires the product to produce.
Data and label foundation
Alert histories, dispositions and investigation reasoning structured into something that supports supervised learning.
Build with explainability as a feature
Models producing investigator-usable reasons, with recall measurement instrumented from the start.
Customer shadow running
On real customer alert populations, measuring reduction and recall together.
Productisation
Version pinning, change notification, validation documentation pack and per-customer performance reporting.
What you receive
A product that survives your customers' model validation, with evidence they can hand an examiner.
Validation documentation pack
Assumptions, methodology, lineage and limitations, in the form a bank's model risk function expects.
Explainable alerts
Reasons an investigator can write into a case file, not a confidence score.
Recall measurement
Instrumented sampling of suppressed alerts and back-testing against known outcomes.
Per-customer performance
Evidence on their population rather than aggregate across your book.
Version control and change notification
Pinned versions, because silent updates break a customer's validated state.
Regulatory mapping with citations
Every obligation traceable to source text, with effective dates structured.
Is this the right starting point?
Worth being direct. There are situations in regtech where custom AI work is the wrong spend, and those are listed rather than buried.
Worth doing if
- Your customers' model validation is a bottleneck in your sales cycle.
- You claim alert reduction and cannot currently evidence preserved recall.
- Screening false positives are your customers' loudest complaint.
- Regulatory change monitoring needs mapping to customer controls rather than retrieval.
- You need explainability as a product feature rather than a compliance afterthought.
Do something else if
- You want to auto-close alerts without a recall story. Customers cannot buy that.
- You have no access to customer alert histories or dispositions. The labels are the product.
- Speed to market is the priority over evidence. In this vertical the evidence is the market.
- You are unwilling to pin versions. Silent updates break your customers' validated state.
Frequently asked questions
Marked up with FAQPage schema so these answers can surface directly in search results and inside AI assistant responses.
What do our banking customers actually need from us?
Model documentation their model risk function can validate — assumptions, methodology, lineage, limitations — plus performance evidence on populations resembling theirs rather than aggregate figures, explainability an investigator can write into a case file, recall evidence supporting any reduction claim, pinned versions with change notification, and ideally shadow running on their own alert history. The revised interagency guidance makes vendor model validation explicit, so this is procurement gating rather than a nice-to-have.
How do we evidence that alert reduction did not suppress true positives?
By instrumenting it. Sample suppressed alerts and have them investigated conventionally, back-test against known outcomes including historical suspicious activity reports, and report recall alongside reduction as a standing metric rather than a launch study. Any model can cut volume; the vendors who win are the ones who can show what went with it.
Is there really no good evidence that AI fixes AML false positives?
There is remarkably little published, which is striking given how large the problem is — over half of banks above a twenty per cent false positive rate, a quarter above forty, most spending an hour or more per alert. Industry surveys mostly report what institutions expect AI to deliver rather than measured outcomes. For a vendor that is an opportunity: measured before-and-after evidence on real customer populations is a differentiator almost nobody currently offers.
Should our product auto-close alerts?
Not as a first move. Auto-closure is a supervisory conversation your customer has to have and many will not, whereas ranking, batching and enriching alerts captures most of the investigator time saving with far less exposure. Build the ranking product, prove recall over time, and let auto-closure be something a mature customer opts into with your evidence behind them.
What is the biggest risk in regulatory change monitoring?
Confident wrongness. A missed change is a gap a customer may catch elsewhere; an invented obligation costs them money, embarrasses their compliance team and ends the relationship permanently. Every assertion must trace to source text with a citation, effective dates and consultation status must be structured rather than inferred, and the system should decline to answer rather than guess.
Related verticals
Organisations in regtech usually share data, buyers or regulators with these. All fourteen are listed on the Financial Services & Insurance page.
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Read more →AI for Audit and Assurance Firms
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Read more →AI for Retail Banking
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Read more →Tell us what the problem looks like.
Thirty minutes, no charge, no deck. We will tell you whether this is an AI problem, a data problem, or a process problem — and we will say when the honest answer is to buy something rather than build it.