Navigating The Path to NextGen AML Detection
Despite more stringent anti-money laundering (AML) regulation over the past decade, combating financial crime continues to be a major problem for financial institutions. Natwest is the latest bank to be under regulatory scrutiny but there have been a multitude of incidents during the past year as the pandemic accelerated the pace of digitalization which in turn opened the door even wider for fraud and money laundering.
In fact, regulators issued more than $10 billion in AML fines to global
financial institutions in 2020, a 26% hike from 2019 figures, according to
research from Fenergo. Breaking it down, this translates into 198 fines for
AML, Know your Customer (KYC), data privacy and MiFID (Markets in Financial
Instruments Directive) breaches.
The fines are not the only damaging fallout. Customer trust is broken,
business is disrupted and opportunities to leverage operational efficiencies
are overlooked. While overcoming these issues may seem insurmountable, the main
stumbling blocks seem to be continued reliance on outdated transaction
monitoring systems (TMS) as well as manual human processes that cannot
distinguish the noise from the real threats. As a result, criminals are able to
break through a bank’s defenses.
Retrieving Unknown, Valuable Information
In fact, our research has shown that there is at least double the
information content in existing data sets that is currently bypassed by TMS
detection systems. Existing systems tend to focus on short term behavior and be blind to
sophisticated schemes that build over periods of a year or longer, and crucially
they cannot “follow the money” or deal with the information content in complex
business ownership structures. They are simply not fit for purpose given the
nature of the problem.
These are not new issues, and many financial institutions are well aware
of the problems. However, there is a fear that they will have to go back to the
drawing board and invest in a complete infrastructure overhaul. This is not
only costly but also time consuming, However, as indicated in our first blog, modernizing
systems could be akin to a self-driving car. It is not a reinvention of the
wheel but instead an enhancement of the technology already in place to offer a
more optimal driving experience. It uses
a combination of AI components that can sense, monitor, and adapt to the
changing road conditions and alert the driver when action is required.
Creating a Holistic Roadmap to Safety
It is the same with Ayasdi Sensa-NetRevealAML™. Just as with the self-driving car, there is no total redesign, but the detection technology is overlaid onto a financial institution’s prevailing framework. It uses the data already in the current TMS process and leverages AI to provide a holistic risk-based map of the dangers that lurk within a bank’s customer behavior. This helps banks better identify patterns and unpick the complex money laundering web of transactions, money flows and relationships which in the past were a blind spot.
The system can detect previously hidden risks with accuracy rates of 90% and detect complex schemes a year earlier than existing processes. At a holistic level the bank can see a more accurate reading of risk with up to 60% reduction in false positives, and a roughly 20% improved total risk coverage in terms of level 3 investigations and suspicious activity reports (SARs).
As the pandemic has shown, the direction of digitalization is only one way and financial institutions that do not improve their oversight and detection rates will go off the grid. They will be overtaken by either newer or existing players who can offer a secure and safe environment to conduct business.