Combating Money Laundering
Combating Money Laundering
Abstract: As federal banking regulators intensify their scrutiny of Bank Secrecy Act and Anti-Money Laundering compliance, community banks need to become more proactive in combating money laundering. One potential tool worth considering is data visualization software. This article examines recent compliance requirements and how to effectively incorporate data visualization software into a bank’s antifraud lines of defense.
Data visualization helps banks combat money laundering
As federal banking regulators intensify their scrutiny of Bank Secrecy Act and Anti-Money Laundering compliance, community banks need to become more proactive in combating money laundering. One potential tool worth considering is data visualization software.
Increased emphasis on BSA/AML
Several recent developments reflect the federal banking agencies’ increasing concern about Bank Secrecy Act and Anti-Money Laundering (BSA/AML) compliance efforts:
- In July, the Financial Crimes Enforcement Network (FinCEN) introduced new customer due diligence (CDD) rules that require institutions to incorporate beneficial ownership identification requirements into existing CDD policies and procedures.
- In its Spring 2016 Semiannual Risk Perspective, the Office of the Comptroller of the Currency (OCC) alerted banks to increasing BSA/AML risks associated with technological developments and new product offerings in the banking industry.
- In recent months, regulators have been scrutinizing automated monitoring systems used by banks to detect suspicious activity to ensure that they’re configured properly.
And don’t assume that regulators are limiting their heightened scrutiny to larger banks. The OCC’s report noted that some large banks are restricting certain customers’ activities or closing their accounts because of BSA/AML concerns. Displacement of these customers, the report warned, “may result in higher-risk customers moving to smaller and less sophisticated banks . . . that potentially have less experience managing the associated BSA/AML risks.”
Banks that fail to take reasonable steps to detect and prevent money laundering activity risk not only government fines, but negative publicity and reputational risk.
Seeing the big picture
Data visualization software — also known as visual analytics — can be a powerful AML tool. Traditional AML software products and methods do a good job of detecting known AML issues. But data visualization software, which is commonly used as an antifraud weapon, excels at spotting new or unknown AML activity.
As criminal activity becomes more sophisticated and more difficult to detect, traditional AML software or methods may no longer be enough. Data visualization software creates visual representations of data. These representations may take many different forms, from pie charts and bar graphs to scatterplots, decision trees and geospatial maps. Visualization helps banks identify suspicious patterns, relationships, trends or anomalies that are difficult to spot using traditional tools alone. It’s particularly useful in identifying new or emerging risks before they do lasting damage.
Criminal enterprises that wish to launder money typically use multiple entities and multiple bank accounts, both domestic and foreign. Using data visualization software, banks can map out the flow of funds across various accounts, identifying relationships between accounts and the entities associated with them. Data visualization can reveal clusters of interrelated entities that would be difficult and time-consuming to spot using traditional methods.
These clusters or other relationships don’t necessarily indicate criminal activity. But they help focus a bank’s AML efforts by pinpointing suspicious activities that warrant further investigation.
Get your data in order
Perhaps the biggest challenge in taking advantage of data visualization software and other automated AML tools is the fact that, at many institutions, information is scattered among many separate systems. For data visualization to do its job, the first step is to collect and integrate this information into a single database. Once this is done, data visualization software can help your bank detect potential AML issues more quickly and effectively.