How to Mitigate Money Laundering Using AI

August 2, 2021

Money laundering detection has become an increasingly sophisticated field over recent years. Advances in anti-money laundering software for banks have enabled many providers to mount a robust fight back against fraudsters, while new-generation merchant fraud detection systems are preventing a wide variety of attacks from occurring in the first place.

But what has also become clear over the years is the extent to which artificial intelligence is set to play an increasingly important role in fraud monitoring, transaction fraud detection and anti-money laundering measures in the future.

Just as the key to an effective fraud monitoring system lies in its ability to automatically evolve and adapt to new threats, AI and machine learning are already playing a role in the mitigation of money-laundering activities.

A Lucrative Criminal Enterprise

The sheer size of the global money-laundering business is no less than remarkable. Accurate figures are difficult to come by, but it is nonetheless estimated that the total combined value of the illegal money laundering sector is around $2,000 bn.

Remarkable, no more than around 0.2% of this activity is detected and dealt with. This, therefore, means that up to 99.8% of all illegal money laundering activities worldwide are successful, highlighting the importance of taking a proactive stance against fraud.

Subsequently, more banks and financial businesses than ever before are beginning to integrate artificial intelligence and machine learning solutions to improve their fraud detection processes.

The Conventional Approach to Tackling Money Laundering

Limitations in the conventional approach to detecting and preventing money laundering have been apparent for some time. The use of rule-based systems like FICO, Fiserv, SAS AML or Actimize by investigating teams has been the norm, with a three-step approach to identifying suspicious transactions:

  • Step One: The alerting system generates an alert
  • Step Two: The alert is reviewed by an investigator using data from multiple sources
  • Step Three: The alert is confirmed as a true positive or declared a false positive

The primary issue with this conventional approach is the way in which it has a tendency to result in far too many false positives. In fact, estimates suggest that anything from 75% to 99% of all outcomes with rule-based systems are false positives.

As a result, businesses and investigation teams collectively waste massive amounts of time, effort and money investigating alerts that turn out to be of no consequence whatsoever.

One of the main flaws with the rule-based system is the way in which even the most recent updates to the ‘rules’ in the system can become quickly outdated - coupled with the impossibility of continuously updating the system in real-time.

Reducing False Positives with AI

Fraudsters and criminal entities are in no way oblivious to the obvious flaws in these rule-based systems. Each time systems are coded with new rules, it rarely takes criminals long to figure them out and adapt to them. By tweaking their approaches to avoid detection, they are able to circumvent rule-based systems with relative ease.

This is one of many areas in which the integration of AI technology can prove invaluable. Rather than being based on rules in a somewhat binary capacity, artificial intelligence and machine learning can identify suspicious transactions and irregular activities far more meticulously and accurately. They can also be used to assign more precise priority levels to the irregularities detected (low, medium or high), so that investigators know how to assign their time more efficiently.

Most importantly, machine learning effectively reverses the ‘cat and mouse’ game, with regard to behaviour modification. Each time the criminal or fraudster modifies their approach to circumvent potential weaknesses, the AI automatically adjusts to compensate. Instead of the fraudster typically being one step ahead of the fraud detection system at all times, the power is passed back to the organization.

Along with continuously learning lessons through the real-time monitoring of transactions and behaviours, AI systems are also refined and improved by the investigators using them on an ongoing basis.

Reduced Investigation Times

The extent to which the integration of AI and machine learning stands to save an organization time and money will always vary from one business to the next.  Though in all instances, dramatically reducing false positives and providing a more diverse set of alerts only stands to bring about positive improvements.

Estimates vary, but it is nonetheless possible for AI to reduce investigation times from several weeks (or even months) to a matter of seconds. All while reducing or even eliminating the risk of human error from the equation, further empowering the organization to allocate its resources more efficiently.

In Summary

Money laundering activities directly fuel human trafficking, drug syndicates and terrorist groups. The importance of adopting the strongest possible stance against money laundering therefore cannot be overstated.

You need only consider the extraordinary value of the global money-laundering sector to understand the enormity of the task facing those charged with combating it. But what’s become evident over recent years, in particular, is how the biggest issue of all is a combination of ignorance and naivety among millions of businesses worldwide.

The more sophisticated criminal entities become, the greater the importance of leveraging AI and machine learning technology to its full capacity. Traditional approaches to fraud detection and prevention are becoming dangerously outdated, making now the time to consider stepping up to something more robust and reliable.

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