BY Alex Baghdjian
Of all the industry groups we engage with, ACAMS is different. The mission focus, the educational framework, and the overall community make its events must attend for practitioner and vendor alike. The Las Vegas flagship event dominates the fall calendar and so we were back in sin city again this year – but with an even bigger team of domain experts. Our continued success in financial crimes has attracted some of the best and the brightest in the industry and we wanted to open by introducing that team:
Leading the group is our VP and Global Head of Financial Services, Doug Stevenson. Doug is a certified anti-money laundering specialist (CAMS) and certified financial crimes investigator (FCI), Doug joined Ayasdi from Pitney Bowes where he served as the General Manager of their Global Financial Crimes and Compliance business unit.
Doug has assembled an amazing team of fellow financial crimes specialists.
Those include David Brooks who formerly ran the N.A. Financial Crimes Practice for Capgemini. His rich experience includes leadership roles at NICE Actimize, Fortent, and Mantas.
The team also includes Chief Architect, Sridhar Govindarajan who, like Doug, hails from the successful Pitney Bowes team. Prior to Pitney Bowes, Sridhar worked in AML and architecture roles at Citi, Deutsche Bank, and Credit Suisse.
The data science lead for financial crimes is also a recent addition. Lei “Ray” Mi comes to Ayasdi from HSBC where he was the Vice President for AML Transaction Monitoring and Global Risk Analytics. In that role, Ray pioneered the use of machine learning and AI for HSBC’s AML function and was a key participant in the creation of the bank’s center of excellence in AI and Machine Learning.
When you have this much talent, you generate a lot of great thinking. That was on full display when we got together as a team to talk about this year’s event. Here are the takeaways on the ACAMS Vegas show.
1. Artificial Intelligence is Going Mainstream
Though many vendors are touting the use of Artificial Intelligence (AI) in their AML offerings, production deployment remains the litmus test of acceptance. Compared to previous years, we found a far greater appetite for and far more financial institutions (FIs) actively experimenting with Artificial Intelligence in their AML programs. Though the number of FIs actual moving from AI experimentation to production is still modest, we expect this number to grow considerably in the coming quarters.
This growth is a function of two drivers.
First, FIs are becoming more sophisticated and are actively sharing information and know how within the community – which is to be expected in a community who doesn’t really “compete” with each other as they do in other lines of business. This manifests itself as a more informed AML buyer. In the past, discussions would revolve mainly around high-level subjects, such as the definition of AI and the differences between supervised and unsupervised learning. However, this year, AML experts wanted to dive deeper and learn more about specific ways in which AI was solving real problems in AML programs. This only gives us more confidence that AI adoption in AML will begin to boom in the coming years.
Second, the vendor community is becoming more adept at building deployable solutions – applications vs. use cases. This allows these POCs to move from the innovation area to the business far faster than before.
Smarter buyer + better product = more deployments of intelligence in financial crimes.
2. Segmentation as Lens in Behavior
Over the last two years, Ayasdi led the dialogue in the AML community from tackling money laundering with a traditional (and increasingly ineffective) rules-based approach vs. a more effective (and efficient) customer behavior-based approach.
This message is gaining widespread adoption amongst both FIs and vendors – with Verafin’s panel correctly stating, “setting a rule doesn’t indicate a behavior.” Foundational to any strong AML program is a customer’s behavior – not a static risk rating or rules-based segmentation.
Much of the work we are doing in this space revolves around collaborating with the FI to determine a) the right data b) the right features c) the resulting customer segments that accurately reflect behavior.
As we do this work, it is clear to the FIs (and the regulator) that accurate segmentation is critical to a successful AML program. It should also be noted, that while accurate segmentation is imperative if you can’t explain it, it borders on useless. This is one of the knocks on AI in AML and another area where Ayasdi has been a pioneer.
3. Change in Behavior is Key
After speaking with one AML/BSA officer at ACAMS, he looked at us and said “Wow! That is the view I’ve always wanted to have!” What we had shown him was how Ayasdi’s AML offering could take a customer behavioral segmentation and add the variable of time to see how customer’s behaviors changed over a certain period.
This is very important and will define the conversation in the coming years.
As noted, an accurate customer segmentation provides a lens into customer behavior. How that behavior changes over time is even more valuable – and not just to the AML team. If a customer moves from one group to another – there is information encoded into that move. Did the customer become riskier to the FI? Did they change how they used certain products and with what frequency? These are powerful insights that have eluded the industry – primarily because of the complexity involved in the challenge.
The implications are profound for the $80M a year KYC refresh component. Change in behavior can make this more effective and efficient – eliminating diligence on those customer’s whose behavior is the same while initiating diligence on those whose behavior has changed in a material fashion.
These are exciting times for the financial crimes community. The technology has never been better, the talent level never higher.
We see progress everywhere – with our clients, our partners like Navigant, the talent joining our team. We look forward to our ACAMS webinar in January where we can expand on the subject of change in behavior.