The era of unlimited budgets to combat money laundering appears to be over. Banks are now seeking ways to not only operate more efficiently, but also to leverage the vast amounts of customer data they’ve accumulated – ultimately benefiting both the customer and the banks themselves.
Artificial intelligence plays a key role in this transformation, but efficiency can also be gained by deploying employees in new ways. The combination of technology and a fresh approach to workforce utilisation presents new opportunities, though implementation comes with its own set of challenges. We spoke with Michiel Droogsma, Associate Partner at Valcon, to explore this further.
A turning point in the fight against money laundering
The fight against money laundering has reached a turning point. After years of rapid growth in Financial Economic Crime (FEC) departments, some banks are now beginning to scale back.
“Several banks have now reviewed their entire customer portfolios,” explains Droogsma. “Strengthened by the green light they received from De Nederlandsche Bank to transition to a risk-based approach, the focus is increasingly shifting towards the most efficient and effective compliance with the Anti-Money Laundering and Terrorist Financing Act (Wwft).”
Cost reduction plays a significant role in this shift. Following substantial fines, banks have also invested heavily in bringing their anti-money laundering policies up to standard.
Amid this extensive effort, a silver lining is emerging: the enormous sums invested have led to the accumulation of valuable customer data. And as we now know, such data is crucial for deepening customer relationships. “All that data – beyond its use in anti-money laundering—has largely remained untapped until now,” says Droogsma. “This represents significant potential for both customers and banks.”
Banks are now facing two key questions: How can we make our Anti-Money Laundering (AML) approach as efficient and effective as possible? And how can we utilise the customer data we’ve collected to maximise customer relationships? “In answering both questions, banks see a key role for AI and the employees within their FEC departments,” says Droogsma.
More efficient and better
First, the optimisation of the AML approach. While Droogsma does not expect AI to fully replace human judgement – particularly in complex cases – he does foresee a significant shift.
“Where analysts currently create risk assessments themselves, AI can generate fully automated reports based on the available data. The role of the analyst then shifts to interpreting and validating the outcomes.”
“Only when the first fines are issued will the AI rules truly become clear.”
AI also contributes to improved quality, according to Droogsma. “AI can simply observe much more because you can train models – and it always remains objective. This objectivity helps reduce human biases, which inevitably creep into decision-making processes. However, there is a risk if incorrect data is used. It’s crucial that customer data and models are properly set up before AI can be used to improve quality.”
The technology is increasingly being used to support analysts, although full integration into their processes has not yet been achieved. “Banks are still cautious, as they’re not yet entirely certain whether they will comply with the new set of rules under the AI Act, which comes into effect in August 2025.”
From compliance to expanding customer relationships
By supporting analysts in their tasks, AI also helps to address the second key question: how can banks best utilise accumulated customer data to strengthen customer relationships?
“For example, only 10 to 15 per cent of business customers have a relationship manager,” explains Droogsma. “For the remaining 85 to 90 per cent, there’s a huge opportunity to use the collected data for better advice and commercial activities.”
If AI takes over part of the anti-money laundering work, (former) AML analysts can shift their focus towards capitalising on this opportunity. However, this requires a cultural change within the organisation. “We need to move from an internal focus – where the emphasis is on delivering high-quality risk assessments – to a culture where we proactively consider how we can better advise customers.”
“Banks are primarily focused on the technical aspects of AI and less on its long-term impact on the organisation.”
This evolution significantly changes the role of the analyst. “The transition to a risk-based approach was already a major challenge for analysts. Now, they not only have to let go of the work they’ve been doing for years but also begin engaging with customers in a way that is largely new to them. They must not only assess risks but also identify opportunities – for both the customer and the bank.”
“This requires a completely different mindset and skill set,” he explains. “We need to train employees not only to delve into the data but also to translate those insights into valuable advice for customers.”
Management lagging behind
Successfully implementing AI solutions brings its own challenges – challenges that, according to Droogsma, many banks have yet to fully address.
“You can see that banks are working on AI technology, but they’re paying far less attention to its impact on the organisation and how to respond to it. Yet it’s incredibly important to truly understand the influence AI will have on workplace culture and the skills employees will need.”
He emphasises that management, in particular, has some catching up to do. While leadership shapes strategy and sets AI-related policies, it often lags behind in hands-on usage.
“Of course, the creative process around AI deployment is in full swing – but that process largely takes place on the work floor. Research from McKinsey shows, for example, that ‘regular’ staff are using AI-driven techniques three times more often than executives.”
“Inspiration sessions are not enough; you need to take it a step further.”
“This can definitely be improved,” he concludes. “To truly understand AI and its implications, leadership must embrace and actively promote it. And by that, I mean not only using AI tools themselves, but also closely observing what’s happening on the ground – with colleagues and with competitors. These insights must then be translated into a clear view of the impact AI can have on the organisation.”
This translation is crucial, says Droogsma – especially when it comes to training and development, including for management.
“Inspiration sessions are not enough. You need to take it a step further. What will AI mean for you? What will you do differently in your work? And how do you really feel about it? Ultimately, everyone must engage with it. This means people need to decide whether this still fits them.”
Faster innovation
In summary: the opportunities presented by AI are vast, but so too are the challenges. Valcon is well-positioned to support organisations in navigating these, says Droogsma.
“We have the knowledge and expertise to map out the entire AI chain – from unlocking and optimally utilising data to understanding its impact on the organisation, including the development of soft skills.”
Bringing all of these elements together is the true art, he explains. “Organisations that manage to do this are much quicker to identify what they really need in order to successfully implement technologies like AI. This enables them to adapt more rapidly where needed, ultimately accelerating innovation.”
“Think end-to-end about innovations like AI,” he advises. “Don’t just focus on the data or the underlying technologies. Map out the benefits and consequences as broadly as possible – for both the customer and the organisation – so you can adopt innovations more quickly. Only then can you truly harness the full potential that AI offers.”
Source: Banken.nl, 7 April 2025: “Wie end-to-end nadenkt over AI, innoveert veel sneller”, by Michiel Droogsma | Valcon
