Fight money laundering with visual analytics
Money laundering is an insidious and ever-present issue for community banks. Rapidly advancing technology enables criminals to invent new ways of gaming the system. But that same technological progress, in the form of data visualization software, can give your community bank an edge in detecting and preventing money laundering and ensuring your institution’s compliance with the Bank Secrecy Act and Anti-Money Laundering (BSA/AML)
Banks that fail to take reasonable steps to detect and prevent money-laundering activity risk government fines. They also may receive severe negative publicity that harms their reputations.
Several developments over the past few years reflect the federal banking agencies’ increasing concern about BSA/AML compliance efforts. For one thing, the Financial Crimes Enforcement Network (FinCEN) introduced customer due diligence (CDD) rules that require institutions to incorporate beneficial ownership identification requirements into existing CDD policies and procedures.
In 2016, 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 addition, regulators increasingly have been scrutinizing automated monitoring systems used by banks to detect suspicious activity to ensure that they’re configured properly.
For several years now, regulators haven’t limited their heightened scrutiny to larger banks. In fact, some large banks have restricted certain customers’ activities or closed their accounts because of BSA/AML concerns. As a result, higher-risk customers often have moved to smaller banks with less experience managing the associated BSA/AML risks.
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.
To counter today’s sophisticated money-laundering schemes, community banks need to consolidate their databases and stay up to date on the latest technological tools at their disposal. By using data visualization software to map trends, clusters and relationships that would be difficult to discern otherwise, community banks can more quickly and easily detect potential money-laundering activities — and take steps to head them off.