The intersection of man and machine – and which shoulders more of the work going into the future – is not just a concern for the legal professional, but also those in anti-money laundering (AML).

As Markus Schulz, global head of FCC Controls, Group Financial Crime Compliance, Standard Chartered, noted, AML professionals ask the very same question: “What will this do to our industry?”

This was the core focus of today’s ACAMS 2016 panel, “Man vs. Machine: Balancing Automation and Manual Reviews to Strengthen Risk Monitoring.”

As Matthew McLaughlin, director of Global Investigations, AFEX, noted, the machine in the discussion isn’t about replacement of the human, it’s about scalability. This is a particular focus as monitoring of the risk/fraud industry continues to shift online.

“Automation is the fraudster’s best friend,” he added, noting that AML professionals have to know the bad guys because they use weak points in the system – like email – to commit their crimes. And while these perpetrators work to “game the system,” AML professionals need tools and technology to stay ahead of them for authentication and other common financial mechanisms.

This is where the machine is needed.

As Vikas Agarwal, principal, PwC, described, advanced analytics and process automation are the new frontiers in AML. He noted that many of the technologies that are taking hold in the industry are where technologies take small steps, like the number of searches investigators run. From there, access to cloud-based data and big data can be applied to look for patterns and trends in risk. As automation comes into the fold, Agarwal notes that there are fewer errors, improved business continuity and cost savings.

Kristin Milachanowski, Ph.D, CAMS, chief data scientist, Digital Economy Analytics, noted that as larger firms aim for 50 to 55 percent automation – a standard goal cited earlier – that doesn’t mean that there will be a related reduction of people, instead this will free humans to focus on other areas and apply their insights in more valuable places.

A recent example of having the ability to optimize data was the release of the Panama Papers. Milachanowski noted that having access to technology meant that AML professionals could search and review that huge corpus of data.

“The machines are doing what you design them to do,” she added.

Once upon a time, a prohibiting factor for looking at AI was cost. But as Agarwal described, what is changing attitudes is that data storage costs are cheaper and the skills needed to dive into that data has “increased exponentially.” Some of this is being driven by compliance, he added.

This is an easy way to land “a big win” to justify the spend on technology, Milachanowski noted.

Agarwal went on to caution that as organizations accept automated processes, they will then need to monitor the output, test it and show to managers and regulators that it’s working.



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