Data Analytics for MSBs to Detect Money Laundering

October 16, 2019

In a past presentation, Andrew Simpson, Chief Operating Officer, 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.

 

Life as an MSB

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.

 

Advanced Analytics for Anomaly Detection

It’s important for MSBs to profile their customer information and transactions by refining and segmenting the data. This will position MSBs to use advanced analytics to detect anomalies.

Segmentation will also allow MSBs to break customers into groups and verify that they are behaving similarly. Attributes such as the frequency of transactions, location, time/day of transactions, source and destinations are areas where anomaly detection models can be built to find transactions.

With good detection, once a transaction or outlier is found that doesn’t match the normal behavior, the MSB is alerted and can examine the transactions to determine whether the behavior is abnormal.

 

Strategies for Detection

Some of the models that MSBs can implement to improve their anomaly detection include the following:

  • Network Linking – Network linking improves risk model construction. This strategy detects customers that may not have exhibited suspicious behavior but are linked to a high-risk person. As a result of this relationship, that customer should inherit some of their risk, causing their own risk score to increase.
  • Predictive Analytics – Predictive analytics use machine learning to train models and predict outcomes. By remembering the patterns or suspicious activity around customers, the model can predict future outcomes about that customer. With a case management system, staff are alerted to look at the pattern and flag it as positive (confirming suspicious activity) or as a false positive, and correct the model to improve future predictions.
  • Merging the Models – This approach merges rules-based models, scenario-based models, anomaly detection and predictive analytics, and is then leveraged by the risk-scoring engine. With this model, make sure to look at the customer’s cumulative score and behavior rather than only one analytic on a particular transaction. A customer may have multiple lower-risk transactions that may cumulatively amount to a high score.

The risks and rewards in the MSB business are big. While MSBs can be profitable for retail stores, it’s important to be aware that the space is often targeted by criminals because there is such a large volume of transactions—making it easier to disguise money laundering.

Advanced analytics help MSBs get ahead of risk by drawing attention to behavior they did not know they should be watching for. When combined with case management and automated regulatory reporting capabilities, MSBs can fulfill their compliance requirements and be on the path to a lucrative business.

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