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June 16, 2021 - Chandreyee Chakravarty

An Exploration of the Convergence of AML and Fraud

Money laundering was a threat long before our current era of online shopping, online banking, and person-to-person digital payments. But digital transformation and technology have increased the size, speed, and scale of how, when, and where money laundering activities take place. 

AML leaders can no longer continue throwing bodies at the problem. Rather, it’s time to seek out tools and technology that will drive operational efficiency and effectiveness in seeking out and stopping AML. Coincidentally, those same tools and resources can also be effective in fighting fraud as a whole.

The Convergence of AML and Fraud Detection

In the past, AML and financial fraud have largely existed in separate conversations. Financial fraud is historically believed to be an isolated business problem, as fraud losses directly impact an FI’s bottom line. AML, however, is rife with compliance challenges and falls under intense regulatory scrutiny. As such, many FIs treat the two as existing in silos.

Because AML and fraud have often been considered different mountains to climb, FIs have leveraged different technology, processes, and strategies for each. Fraud detection has largely relied on the FIs’ own data and data shared by other FIs to identify fraudsters when they come through the door. They can use this data to create and refine analytic models to detect fraud.

By contrast, AML has largely been detected by reviewing individual customer and transaction data over time and identifying behaviors that might align with money laundering. This information is packaged into reports and sent to financial investigation agencies for further review. Or, put another way, there is no stopping AML at the gate with traditional processes.

However, this siloed approach is starting to go the way of the dinosaur, as noted by Aite Group’s report, Key Trends Driving AML Compliance Transformation in 2021 and Beyond

Of the 15 FIs surveyed in the report, 12 highlight the need for AML and fraud convergence. A key theme in the report is the need for better collaboration to fight against all forms of financial crime, including fraud and AML. This can be achieved with the sharing of information, data, and intelligence, all of which can be enabled when fraud technologies are aligned. 

Fighting AML and Fraud Together

The convergence of people, processes, and tools creates a cohesive approach to fighting AML and fraud. 

It takes a collaborative effort among people to make sense of the data harnessed by technology, gain holistic insight and context into customers and their transactions, and take action against suspicious activities. The right processes and technology can uncover hidden risks, speed up the process of identifying threats, and reduce false positives. Together, this convergence can improve the way organizations monitor for and respond to all types of financial crime.

However, it’s worth noting that convergence is rarely simple. There are no best practices or standard structures that work across all FIs. Even the term ‘convergence’ can mean different things to different organizations. The one common thread is the idea that knowledge is power, and better ways to share information and eliminate silos is key to success.

How DataVisor Helps FIs Combat AML and Fraud at the Source

Both of the top challenges (too many false positives and harnessing internal data) for FIs can be addressed with DataVisor’s multi-layered approach to fraud. Using a combination of supervised and unsupervised machine learning (SML and UML, respectively), linkage analysis, and device intelligence, DataVisor detects potential AML and fraud in real time and can help fraud teams stop many forms of financial crime at the source. 

DataVisor looks at all transactions and deposit patterns over time and finds the most suspicious cases with greater accuracy. Companies can respond to threats as they evolve, and since Unsupervised Machine Learning doesn’t require labels or input, it doesn’t require constant retuning to detect new threats. Even sophisticated threats and the use of multiple money mules to sift money through many accounts can be uncovered with DataVisor’s multi-point analysis. 

Request a demo to see DataVisor fight fraud.

about Chandreyee Chakravarty
Chandreyee is the Regional Head of Sales at Datavisor. She has held high-profile sales positions within the Identity Management and AI space over the last 20 years globally.
about Chandreyee Chakravarty
Chandreyee is the Regional Head of Sales at Datavisor. She has held high-profile sales positions within the Identity Management and AI space over the last 20 years globally.