Compliance teams today are bombarded by alerts regarding changes in their customer and supplier bases. The challenge is managing this rising volume to focus on what truly matters. Thousands of alerts a day can create a messy, and lengthy, process to figure out whether the risk of onboarded customers has fundamentally changed.
A tension has emerged: These processes that are designed to catch financial crime are generating so much noise that genuine signals are becoming increasingly hard to hear. Criminals are just as clued into compliance processes as the teams themselves, so the gaps in manual review are exactly where risk takes hold.
We cannot keep pace through manual effort alone. That is why compliance teams are increasingly exploring what AI can do to triage alerts and make investigations more focused, efficient and effective.
How Serious Is the Problem?
The trouble is the vast majority of alerts aren’t risk signals; they’re genuine material changes, like a registered address change or a notice that a director has left the board. But buried amongst the daily torrent of updates are the signals that matter, the ones that can fly under the radar but cause tremendous damage.
Where static KYB – know your business – compliance checks are still an issue, the period between onboarding and the next scheduled review is a danger zone for missed signals.
TransactionLink’s Mateusz Pniewski
Consider a payment firm that onboards a new merchant in January. At that initial point, all registry checks pass. But by March, the company’s director had been replaced, and the registered address had been changed. Whether these were legitimate or not changes remained to be seen, but that’s the point: Without continuous monitoring, these updates would go unnoticed for the longest time. Modern fraud is advanced, intelligence-driven and relentless, and that gap is exactly what criminals exploit.
A recent Companies House glitch left five million entities exposed after unauthorized users were able to alter such registered details as director names, addresses and ownership records – all without the knowledge of the companies affected. When the most authoritative registries can be manipulated in this way, firms need confidence in their KYB and ongoing due diligence (ODD) processes to ensure all risk signals are identified in real time.
When Noise Becomes a Risk Signal
An assumption is ingrained in how many organizations approach KYB compliance, that verifying a business at onboarding is enough. Run the check, confirm the details, tick the box, job done. But these static, one-time checks leave companies at risk.
The U.K. Financial Conduct Authority has been explicit that onboarding-only KYB is insufficient as a standalone defense against financial crime, and so firms must be able to detect and respond to material changes in a customer’s risk profile over time.
But it’s still hard to distinguish signals from noise at scale. A risk team cannot investigate every alert in sufficient detail to quickly understand what changes represent a genuine shift in risk, and which are business as usual. This level of assessment needs layered verification, cross-referencing multiple data sources and monitoring in real time. And that extends beyond manual capability.
Scaling with AI
Teams need the tools to allow ongoing assessments that filter through thousands of alerts each day, and AI has the capacity to do so. It can process large volumes of signals simultaneously, identify patterns that human reviewers scanning manually may miss, and surface changes that represent genuine risk.
When used correctly, AI acts as a triage layer: filtering out the noise so that teams can focus their time on the cases that actually warrant attention.
By automating global registry data sourcing, primary document collection, complex ownership tree structuring and enhanced due diligence (EDD) procedures, firms can remove friction from merchant and corporate onboarding while maintaining rigorous compliance standards.
As regulation intensifies and onboarding volumes grow, these businesses need flexibility, not rigid, one-size-fits-all workflows.
There is a broader opportunity here. Institutions that get this right not only protect themselves against fraud, but also create the conditions for faster, more confident growth. It’s reached the point where every new, legitimate merchant waiting to be onboarded is revenue at risk, so slow onboarding is an invitation for competitors to get ahead.
To be clear, deploying AI isn’t intended to be a black box situation. This technology offers firms a way to enhance existing processes to bring new levels of transparency and accuracy that teams can gain greater confidence from.
Mateusz Pniewski is CEO of know your customer (KYC) and know your business (KYB) automation company TransactionLink. With a MEng degree in Manufacturing Engineering from the University of Cambridge, he started his career at Berlin's Rocket Internet, moved to German neobank N26, and spent two years as a Visa project manager before founding the customer-onboarding fintech TransactionLink in 2020.
Topics: Regulation & Compliance, AML & Fraud, Tools & Techniques