Webinar Recap: How to Use Data Analytics to Detect Money Laundering, Part 1

June 19, 2017

In the How to Use Data Analytics to Detect Money Laundering webinar, presenter Andrew Simpson, Chief Operating Officer at CaseWare Analytics, delivered key tips targeted to retail stores considering expansion into money services businesses (MSBs). His advice looks at how data analytics and technology can improve processes, detect common money laundering scenarios common to MSBs, and fulfill anti-money laundering (AML) compliance requirements.

Having staff complete regulatory reports and watch for suspicious behavior is both time-consuming and costly for MSBs. And chances are, if reporting is completed manually then data entry errors are being made and reporting deadlines may be missed, which can lead to expensive penalties from regulators.

To reduce the workload that comes with regulatory reporting and to avoid fines and penalties, MSBs need to consider a mix of analytics, end-to-end automation and case management to detect money laundering.

The nature of the MSB market involves vast amounts of inaccurate information: clients may not be who they say they are, information may be incomplete, or the same ID could be assigned to multiple customers. Analytics look closer, helping ensure that the data is complete. Once the data is accurate and complete, it can then be refined by parsing, standardizing and using fuzzy match. This makes aggregating and correlating the data easier when searching for criminal activity.

With end-to-end automation, businesses can complete 70%–80% of their reporting without involving staff. Analytics can identify reportable transactions, aggregate data, pre-populate and validate reports, submit and then confirm report submissions. The automation also retains evidence, allowing the company to demonstrate it is taking steps to comply with regulations.

Some of the crimes associated with MSBs include human smuggling, narcotics trafficking, terrorist financing, elder abuse, mail order brides and heavenly offerings. These crimes have unique data patterns, which analytics can detect by mining the intelligence.  

If suspicious activity occurs at an MSB with multiple locations, a case management system and analytics can trace it back to where it originated. This both reminds staff that an automated system is in place to track transactions and their activity, and it also relieves the burden on the compliance officer by providing insight into why something went wrong. These insights into the root cause determine whether internal controls working, if more staff training is required or whether the right staff are being notified when certain activity happens. 

By making regulatory reporting more efficient and routinely examining their analytics, MSBs can dramatically improve the effectiveness of their compliance programs.

Watch for Part II of this story, where we’ll discuss the crimes targeted to MSBs and how advanced data analytics can detect the patterns associated with them, helping MSBs proactively address their AML compliance challenges.

 

About Anu Sood:

Anu Sood is the Director of Product and Corporate Marketing at CaseWare Analytics and is responsible for the company’s global marketing strategy. Prior to CaseWare Analytics, Anu worked in various roles in the high-tech industry and her accomplishments range from writing software for telephone switches to launching a new global satellite communication service. Anu has extensive experience in strategic marketing, corporate communications, demand generation, content marketing, product management, product marketing and technology development. 

Connect:   Anu Sood

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