Organizations Are Getting Too Little Value From Analytics
“Through 2022, only 20% of analytic insights will deliver business outcomes.” -Gartner  Organizations are getting too little value from analytics. Billions are being…
Today’s anti-money laundering (AML) and transaction monitoring systems need to be quicker and more agile to identify increasingly complex fraudulent transactions. Due to the rapid evolution of fraudulent behavior, often layered behind seemingly innocuous transactions, AML models require greater sophistication to remain effective. Flexible approaches that utilize advanced computational techniques are needed to adapt to changing fraud patterns and to create effective rules for detection.
Current AML and transaction monitoring efforts may be insufficient for the following reasons:
In December of 2018, the Federal Reserve, Federal Deposit Insurance Corporation (FDIC), Financial Crimes Enforcement Network (FinCEN), National Credit Union Administration and Office of the Comptroller of the Currency (OCC), issued the Joint Statement on Innovative Efforts to Combat Money Laundering and Terrorist Financing. The Joint Statement encourages banks to implement innovative approaches, specifically referencing artificial intelligence (AI). The document states that financial Institutions need to become increasingly sophisticated in their approaches to identifying suspicious activity by building innovative internal financial intelligence units devoted to identifying complex and strategic illicit finance vulnerabilities and threats.
Knowledge Graphs have emerged as an important tool for AML and transaction monitoring. As money laundering involves cash flow relationships between entities, a Knowledge Graph can be used to capture financial transactions. In our Whitepaper, we demonstrate two graph analytics techniques, clustering and label propagation. Clustering can be used to focus investigation on certain high-risk sectors, while simultaneously reducing focus on low-risk sectors. This provides an efficient allocation of analyst resources and reduces false positives. Label propagation helps find previously unknowable patterns that may have been missed by analysts in the transaction monitoring process, thereby reducing false negatives.
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