RegTech Intelligence

Firms should invest in KYC, model “good” customer transactions to reduce transaction monitoring alerts pile-up

Rachel Wolcott, Regulatory Intelligence

Firms should re-engineer their anti-money laundering (AML) systems and controls to refocus on know-your-customer (KYC) processes to prevent the inevitable pile-up of transaction monitoring alerts. Firms’ pivot to digital onboarding has prioritised speed over collecting enough information to determine whether transactions are suspicious. It has created an inefficient, expensive process where AML analysts are sifting through thousands of alerts looking for the proverbial needle in a haystack, experts said.

Some banks, in order to onboard customers digitally in less than 24 hours, rely tools that verify identity based on existing information. These verification tools use customer addresses, credit bureau information, previous bank account details to risk score applicants. Some banks do not ask for photo identification, like a driver’s license or a passport, for customers deemed to be low risk.

Banks can take this approach, because, in the UK at least, there is no minimum standard for KYC checks. The UK takes a principles- based approach and some anti-financial crime professionals believe that is a mistake. There should be a minimum standard to KYC low-risk customers and principles to apply for high risk customers, experts said.

Reactive controls

Firms should get more information from customers and take more time to conduct checks prior to onboarding, even if it is more expensive. They should then use technology, such as artificial intelligence, to build their understanding of customer behaviour to detect the difference between legitimate transactions and actual money laundering.

Transaction monitoring is a reactive AML control, according to Ray Blake, a director at The Dark Money Files in London. “[The controls] kick in only when the bank absolutely has to do something. Whereas proactive controls that took time out when there wasn’t a sense of urgency, when something didn’t need to happen to unlock a transfer, for instance, could be going on in the background, and could actually create huge wins down the road, but because we don’t earn those wins today, we’re not going to do it,” Blake told the Human Risk podcast.

To save time and money, firms do minimal KYC checks, but then hire hundreds of people to wade through alerts, most of which are false positives.

“What we’ve done here is we’ve said this front-end [KYC] process we’ll do as cheaply as we possibly can. It means that we have a problem, but that’s not today’s problem. That’s a problem later down the line when we get more alerts than we need. So now that we’ve got more alerts than we need, we’ll get extra people in to wade through all of those alerts and find the ones that we really need.

“Guess what? The main focus of the technology that’s being introduced is aimed at reducing your false positives by looking at that large pile that you’ve generated and getting rid of some of them for you. Now, is it me or would it be more effective not to create that large pile in the first place?” Blake said.


Following up on transaction monitoring alerts can take an average of two weeks, because either bank branch or call centre staff tasked are tasked with contacting customers to query suspicious transactions, said Uri Rivner, chief executive at Refine Intelligence, an AML software company in Tel Aviv.

Branch staff at one of Refine’s customers were able resolve suspicious transaction alerts without contacting customers in only 12% of queries. The rest — 88% of queries — required multiple calls between the AML team, the branch and customers to resolve.

“Chasing customers over the phone for such inquiries is very high friction, very costly, and often inconsistent. One person in the branch may run the inquiry in a different way than other people, or one investigation officer may ask different questions than another investigation officer,” Rivner said.

Contacting customers digitally is more effective at resolving queries more quickly. Some 85% of customers contacted digitally complete the process and provide information about source of funds, the relationship of the beneficiary and the nature of the transaction. The process usually takes a few minutes, Rivner said.

Continuous KYC, modelling the good

Financial services firms treat onboarding as a procedure that happens once when a customer opens an account. KYC processes are a hurdle to entry into the firm, but once the customer is onboarded, information collection for AML purposes in effect stops. Bearing in mind that customers can stay with a bank for decades, it is a false economy to minimise the cost of KYC while speeding up the process. Firms should keep learning about their customers because learning more about them, more often, will minimise friction in the relationship, Blake said.

Banks have lost their “KYC superpower” because few in-branch staff have personal contact with regular customers now that most conduct their business online and use digital onboarding to open accounts. Customers’ interaction with their banks is very one-sided now. They make transactions, but the bank has no context because it does not know its customers. Losing the two-sided interaction has led to a sharp decline in a bank’s ability to know a customer, Rivner said.

“”Most of the AML alerts, I would say 99%, don’t have that context; so, approaching the line of business does make sense, but the manual approach used today is simply inefficient and bias-prone,” Rivner said.

Firms should model what good transactional behaviour looks like, not just the bad, to get the context with regards to customer behaviour. That means collecting demographic and transaction data to model life events. Is this customer a grandparent sending a cash gift to a grandchild for university graduation? Good AI and machine learning tools will use data sets that help the financial crime analyst see the difference between money laundering and a customer paying cash for a car.

This approach permits firms to “green flag” transactions. It requires tapping into a labelled data set of confirmed genuine, falsely flagged anomalies and their legitimate explanations, Rivner said.

“Why is the customer moving a lot of funds into the account and doing a big international wire transfer? A good AI model will tell you that the most common explanations are perfectly legit real estate or investment activities. Why is the customer pulling thousands of dollars in cash from their account all of a sudden? The top reasons are related to buying a vehicle or doing a renovation project, where contractors ask to be paid in cash,” Rivner said.


Produced by Thomson Reuters Accelus Regulatory Intelligence  24 May 2023

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