Money launderers “wash” more than $2 trillion in the global economy each year, despite the best efforts of financial institutions (FIs) and governments.
Much of their success has to do with the money launderers’ ability to fool legacy anti-money laundering (AML) strategies by stealing the identities of legitimate consumers and using their good names to clean illicit funds, often at the scale of millions of dollars at a time. That’s a change from money laundering strategies that require “money mules” to actively participate in money laundering schemes. Today, with just a few clicks, criminals are creating new ways to commit money laundering crimes, and that new connection between fraud and AML strategy is an important one, requiring a new approach to analytics for FIs.
Those are just some of the insights revealed in Augmented AML And Fraud Risk Management: Analytics Guide To Enhanced Alert Generation, a PYMNTS and Featurespace collaboration. The report details changes in how money launderers and fraudsters operate and how the integration of artificial intelligence (AI)-powered analytics can transform AML strategy.
Money launderers change their tactics in tandem with the development of new technologies. As user experience features such as seamless connections between bank accounts and eCommerce become more common, money launderers have more opportunities to create clever ways to evade legacy rules-based AML flags. A single data breach can allow a money launder to access a victims’ entire digital identity and use it for money laundering activities well before a consumer — or their bank — is aware.
AI and behavioral analytics can help FIs spot money laundering activities at the transaction level. Even when a consumers’ identity has been stolen — or a money mule has been used to launch a money-laundering scheme — sophisticated analytics can spot suspicious changes in consumer behavior before a transaction goes through. While many FIs created their AML strategy long before the era of AI, there are solutions for augmenting existing systems without “ripping and replacing” current tools when a vulnerability is identified.
The right analytics solution can augment existing AML systems while transitioning to more modern tools. The right augmented analytics solution can provide precise understandings that make it easier for organizations to prepare, create and explain insights. This makes it easier for FIs and other entities to tailor transaction risk scores and flag rules to security, user experience and operational imperatives. In addition, access to better data can mean better long-term business outcomes. Augmented analytics do more than improve the accuracy of transaction risk scoring and help organizations identify risk and fortify security — the technology also allows organizations to lay the groundwork for long-term growth and product innovations of the future.
To learn more about FIs can leverage new analytics tools to augment their AML strategy, download the report.
You can also discover why Aite-Novarica Group has recognized Featurespace as Best-in-Class among fraud & AML machine learning platforms here.
Download the full version of the report here.Download Report