It’s Time to Re-think AML Strategies

If you’ve been following the news recently, you’ve probably seen the deluge of headlines surrounding financial fraud and money laundering. Whether it’s Newsweek’s parent company and Danske Bank being charged or Canada, United Arab Emirates and Turkey cracking down on suspicious activity, it’s clear traditional anti-money laundering (AML) strategies aren’t cutting it and there’s a want as well as pressure to do better.

AML systems have traditionally been built piece by piece, essentially copying one approach for different lines of business (retail, corporate, correspondent, trade, markets, etc).  This is done after the fact and responds only to a very limited set of behaviors relating to a single incident. This piecemealing complicates the monitoring process by introducing a range of overly simplistic, siloed solutions that can’t help spot new patterns or behaviors, but can only respond to specific triggers. By the time the news stories are circulated detailing the financial crime incident, criminals have already started to change their behaviors to remain under the radar or moved their activity to a new target bank that has adopted similar controls.

Not only is this approach ineffective at recognizing and stopping financial crime threats, it’s also creates a surprising number of false positives – 90 to 95 percent. With such narrow behavior parameters, normal day-to-day activities get flagged as potential crime and analysts are required by law to investigate every alert, no matter how credible. Banks keep throwing more budget and more bodies at the problem, but are still stung by millions of dollars in fines every year and still inadvertently facilitate money laundering transactions equivalent to 2 to 5 percent of global GDP.

What’s missing from current approaches to AML? Context.

For the purposes of AML, it’s a lot more useful to know who is transacting than the amount that’s being transacted, yet most AML system alerts are built around the latter. Anyone can transact in high volume or frequency (both of which would raise red flags) so that information is only useful when combined with outside knowledge about that entity. If they have common connections with people on the terrorist watch list, that’s going to be worth taking a closer look at versus someone interacting with regular business associates.

Combining internal, publicly available and transactional data, you can complete a full picture for investigators to make a decision on whether something looks suspicious, in significantly less time. If this full picture of activity and context is used in the detection and analytical processes, it effectively combines human intelligence with artificial intelligence (AI). This in turn creates context and makes it much easier to detect activity, reduce false positives, and more quickly escalate the number of real incidents that are captured.

Thanks to AI, it is now possible to look for hidden relationships that could reveal nefarious activity and guiding this process by incorporating human intelligence into the analytical streams. AI can pour through more data faster than its human counterparts, making it possible for banks to find out more about criminal networks than ever before. Transactions leave a data footprint, and analytics can start to paint a picture of what criminals are up to – the ultimate goal to be able to map this activity in near real time.

Already, AI augmentation of the traditional AML process has improved efficiency, reducing false positives by over 98 percent and increasing the number of real incidents detected that went completely missed by old systems. This alone has saved millions of dollars in compliance costs and potential fines. With an increased focus on cracking down on financial crime and holding banks and executives accountable for financial crime, institutions can ill afford to stick to the status quo.

The world is a series of relationships, why not model that information? With investments in AI this is becoming possible. Financial institutions can now move from monitoring to being at the heart of disrupting financial crime and the nefarious activities and industries it underpins.


Add Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

rapper busta rhymes reading speech
How Busta Rhymes Achieved a Net Worth of $65 Million
10 Things You Didn't Know About Rob Glaser
10 Things You Didn’t Know About Rob Glaser
How Steve Kroft Achieved a Net Worth of $17 Million
10 Things You Didn’t Know about EMCOR Group CEO Anthony Guzzi
Chase United Mileage Explorer
20 Benefits of the Chase United Explorer Credit Card
Brookfield Infrastructure Partners
Why Brookfield Infrastructure Partners is a Solid Dividend Stock
Welltower
Why Welltower (WELL) is a Solid Dividend Stock for the Next 20 Years
Chase Slate
20 Benefits of Having a Chase Slate Card
machine translation
How Close are We To Getting Machine Translation Perfected?
Flexible Electronics Technology
The Future is Bright for Flexible Electronics
Productivity 101: Why you Should Consider a Time Tracking Tool
Biosphere 2
Closed Ecological Systems: Can They Save the Future?
10 Reasons You Should Visit Socrates Sculpture Park in NY
Exterior of Old Homestead Steakhouse NYC
Why The Old Homestead Steakhouse is One of NYC’s Finest Steakhouses
10 Things You Didn’t Know about the Staten Island Ferry
10 Reasons To Stay at The Ritz Carlton Amelia Island
1964 Ferrari 275 GTB-C Speciale
A Closer Look at the 1964 Ferrari 275 GTB-C Speciale
1961 Ferrari 250 GT LWB California Spider
A Closer Look at the $18.5 Million 1961 Ferrari 250 GT LWB California Spider
1954 Ferrari 375-Plus
A Closer Look at the $18.3 Million 1954 Ferrari 375-Plus
1964 Ferrari 250 LM Rear
Is the 1964 Ferrari 250 LM Really Worth $18.26 Million?
A Closer Look at the $4.6 Million Louis Moinet Meteoris
A Closer Look at the $4 Million Patek Philippe Platinum World Time
A Closer Look at the $3.3 Million Piaget Emperador Temple
A Closer Look at The Patek Philippe 1953 Heures Universelles Ref 2523