Financial Crime and Compliance (FCC) teams are continuously investing in technologies, processes and resources to fight the estimated $4 Trillion of money laundering that circulates in our global financial systems every year. The coronavirus pandemic has recently compounded the challenge of this fight by materially changing customer behaviours and closing large AML resource centres, which in turn have generated significant alert backlogs.
As part of Nasdaq’s Singapore Fintech Festival coverage, Darren Innes, Head of AML Technology at Nasdaq, spoke about why advanced machine technology matters to the operation of FCC teams, the challenges they face and what the future holds.
Why is Anti-Money Laundering (AML) important to Nasdaq?
An estimated $4 Trillion of money is laundered through our financial systems every year, according to Financial Crime News 2020, and banks globally spend between $38-50 Billion a year to tackle these crimes. At present, however, only 1-1.5% of all laundering is identified and investigated by authorities.
Innes explains that “Nasdaq strives to bring integrity, transparency and efficiency to our financial ecosystems and the challenge to help financial institutions more effectively identify money laundering through technology is one that fits well with our strategy.”
“Having followed the market and its practitioners closely, we identified a clear opportunity for technology to be a catalyst for change, beginning with AML operations. Robotic Process Automation (RPA) has delivered significant efficiencies across many financial services functions, but the automation of more complex money laundering processes has to date been limited.”
How can automated technology help AML?
AML investigations are a complex process that is reliant on scarce expertise to work an increasing number of suspicious alerts generated by monitoring systems, a large proportion of which are false positives.
It is this area of FCC operations in which Innes outlines the focus for Nasdaq: “We recognized that Automation in the AML space should not be about removing people from operations for the sake of it and instead about giving more time and resource for experts to more effectively investigate financial crime.”
“Nasdaq identified a technology built by an organization called Caspian that uses advanced machine learning to replicate complex human decision making and the area that was of most interest to apply this technology was the investigation of AML alerts driven out of transaction monitoring systems.”
Nasdaq Automated Investigator for AML now uses this technology to automatically work these alerts, a high proportion of which are false positives. By training the machine with the decision-making traits of gold standard experts, mitigated alerts can be confidently closed with a fully explainable audit trail. By automatically eradicating high volumes of false positives, human risk analysts can then use machine-based recommendations to work those alerts that are identified as requiring further analysis.
What are the future goals beyond this AML solution?
Innes highlights that “we want to give people the tools to solve the things that are getting in the way of people not actually identifying true financial crime.”
The exciting future opportunity is that this technology can be applied in multiple domains across the compliance operations of global banks to bring greater effectiveness and efficiencies. For example, “the investigation process at the first level of AML is very similar to that of fraud in payments and expansion into such areas will provide wide-reaching value to both our customers and their clients.”