The FinCEN files have shown that it’s time for a change in AML. We must take a completely new approach in order to catch up with the speed of innovation in financial crime.
Despite what you’ll read in news headlines, we can’t lay all of the blame for anti-money laundering failures at the doors of the banks. The majority of compliance teams are doing what they can, and what they are being asked to do.
Historically, AML has, in large part been a box-checking exercise. Banks have weaved through mountains of false alerts, investigated cases, sent SARs, and then got on with business as usual. In some jurisdictions, banks can‘t even interfere with customers under investigation, in fear of jeopardizing cases.
But the sentiment towards banks’ responsibility in AML is changing. They are increasingly looking at AML as a corporate social responsibility issue and even a competitive advantage. Banks are looking to protect their brands from the horrors of an AML scandal, and as such are taking a more proactive approach.
They are also throwing a lot of money at the problem. Deutsche Bank claims to have invested close to $1 billion in improved AML procedures and increased its anti-financial crime teams to over 1,500 people. Most big-brand banks have a similar story to tell.
"With reputation on the line, better AML controls can become good business."
So where does the problem lie?
From the thousands of SARs discovered in the FinCEN files, lack of customer oversight is evident. Banks need to establish a method of knowing their customers through their actions across the organization and beyond the organizational walls. By doing so, banks can better understand AML and compliance risk, which gives them the necessary tools to bar customers from doing business or limiting their activity.
While banks are striving to better enforce regulations by pouring money and resources into CDD and transaction monitoring, forming this type of intelligent customer overview might be the real solution. Proper Customer Due Diligence and customer risk monitoring can only be achieved by continuously tracking customer behavior and transactional networks. With the latest developments in Artificial Intelligence – that is now possible.
But, the reality for compliance teams is they are hindered by outdated technology in their risk assessment and transaction monitoring systems and because of this, banks are fighting a steep, uphill battle against serious organized crime.
In 2019, the Bank of England issued a statement that claimed: “existing (money laundering) risks may be amplified if governance controls do not keep pace with current advancements in technological innovation.”
I know from my time working as a senior compliance technology officer that many traditional AML systems are inefficient, slow and labor-intensive, and often lead to inaccurate outcomes. In fact, most of the systems pre-date the iPhone, so they are using last-generation technology and techniques to detect criminal activity.
In short, legacy AML systems are not fit-for-purpose. Legacy vendors built them for the box-checking world of the past, and they are focused on one suspicious transaction at a time – rather than looking at ‘bad actors’ in the financial system, and patterns in their behavior.
As launderers constantly evolve their techniques to circumvent rule-based or simple statistical detection, the AML systems market has not kept up. There is a dire need for innovation. Unless systems are updated, banks can continue to file suspicious activity reports (SAR), but if bad actors can conduct their business ‘as usual’ and shuffle money around the globe to hide its malicious origin, the effectiveness of a SAR is significantly diminished.
What’s the solution?
I believe we need to rethink our entire approach to AML. We need to empower compliance departments with better controls and oversight, and move away from outdated, traditionally rule-based systems and towards a modern, AI-enabled, behavioral approach.
While the bad guys have learned how to evade rule-based systems, they find it extremely difficult to get around AI algorithms that search for anomalies in behavior. The advancement of AI algorithms, especially in the field of deep learning, provides an opportunity for banks to detect more complex and evasive money laundering networks.
So the answer is to establish continuous automated risk monitoring and implement a workflow system that provides money laundering risk scores for customers.
The latest AI software could kickstart a new age of customer AML risk-based overview. Instead of relying on static and self-reported KYC data, AI systems can analyze behavior over a period of time and compare it with peer-groups and past actions. It provides compliance teams with a continuous risk-rating of their customers, actor insights and summaries to facilitate efficient and thorough investigations, and an organizational-wide overview.
Recent advancements in AI have not only made the above possible, but also practical. Our latest Human AI models contextualize and explain the appropriate data, making it easier for banks to spot sophisticated crime.
By looking at AML not simply as a box-ticking exercise, but as a competitive advantage that can increase customers’ trust in their financial institutions, banks have a lot to gain. Moving towards behavior-based AML systems is a move towards making money good.
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