Oracle Graph Visualization Boosts Fight Against Money Laundering

Oracle Financial Services

has made its in-memory graph visualization tool, Investigation Hub, available in its enterprise case management application and in other third-party AML case managers. Investigators can interactively scrutinize money laundering networks and apply graph analytics to increase the effectiveness of their investigations. Unlike point solutions, this capability also saves on technology integration costs and the need for additional training on new tools.

Research firm Gartner named graph analytics, which is “a set of analytic techniques that shows how entities such as people, places and things are related to each other,” a top trend last year. Many financial institutions are eager to apply this exciting technology—and the visualizations it enables—to better identify hidden criminal networks. At the same time, as regulatory and compliance burdens increase, financial institutions require tools that allow their anti-money laundering (AML) investigators to work as efficiently as possible.

“At Oracle, we are democratizing advanced technologies like graph analytics by making them available in our applications,” said John Edison, vice president, Financial Crime and Compliance Management Products, Oracle Financial Services. “Now, AML professionals, from junior analysts to senior investigators, can explore in-memory graph visualizations without disrupting their workflow. This saves time and leads to smarter decisions, as analysts and investigators have all the tools, information and context they need in one place.”

In addition to the new integration, Oracle also made recent enhancements to its Investigation Hub to help analysts quickly understand both complex criminal networks and normal customer behavior. This allows AML professionals to disposition cases faster and with more accuracy. The recent enhancements include:

  • Enhanced Entity Resolution: Allows institutions to gain a 360-degree picture of their customers and external entities alike by identifying different instances of the same entity across data sources.
  • Network Evolution: Time-lapse visualization of how a network changes over time helps AML analysts rapidly understand and contextualize transaction flows and customer activity.
  • On-Demand Integration with Third-Party Data: Enriches analytics by providing access to risk ratings and factors extracted from structured and unstructured external and internal data sources.

Global financial institutions continue to select Oracle for its enterprise-grade anti-financial crime platform, which is regulator-accepted and based on a common data foundation that takes inputs from any transaction system. This “single source of truth” enables data sciences’ teams to consume data and leverage advanced analytics to monitor, detect and investigate as needed. With Oracle Financial Crime and Compliance Management, compliance teams can also increase overall program effectiveness and optimize compliance operations at scale.

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