Reduce Money Laundering Risk with Big Data Analytics

Keep fraud at bay and remain compliant with shifting regulations using powerful data solutions.

Promote better money laundering detection by:

  • Unifying data silos from legacy systems, providing trusted information.

  • Analyzing large volumes of both structured and unstructured data in real-time.

  • Detecting money laundering patterns across omnichannel touch points.

  • Visualizing data for unstructured searches that can detect anomalies ahead of an internal audit.



New anti-money laundering (AML) compliance regulations have resulted in multibillion-dollar fines for many large financial institutions. As a result, the financial services industry has made fraud detection and compliance top priorities. To keep up with regulations, sophisticated data management solutions are required.

Intel and our partners meet that challenge head on, providing new opportunities for internal operations management and money laundering detection. Many institutions spend more time preparing data than utilizing analytics to make time sensitive decisions. This results in huge losses and inefficiencies. Companies like SAS and Cloudera utilize Intel® technology to streamline data management, analytics, and by extension, decision-making, and regulatory compliance. Faster analytics increases security, making AML operations more powerful than ever before.

Data siloing can be one of the main causes of inefficiency and cost. The financial services industry collects data from many sources, including mobile, online, point-of-sale, and ATMs. Often, this data is fragmented, or only available for a short time. Apache Hadoop* provides the flexibility to ingest and consolidate data from any source, providing secure unification of information.

By introducing an enterprise data hub built on an Apache Hadoop* open-standard, open-source framework, financial institutions have a starting point to analyze massive volumes of structured and unstructured data. These analytic operations can be done in real time, allowing users to identify suspicious trends fast. 

User interaction happens across a multitude of touchpoints, including online, point-of-sale, mobile, and many others. Money laundering can take place at any one of these points, and a data solution that allows for rapid detection is paramount. A robust and powerful predictive modeling solution can be the key to stopping omni-channel money laundering. 

Internal audits can be time-consuming and costly, so a solution that allows for unstructured search and rapid modeling is a no-brainer. With a powerful and agile scenario engine, reporting can be automated to scan for suspicious activity and other anomalies before they become major problems.