Datameer’s analytics platform provides the right combination of power, speed, and flexibility required to successfully navigate the unpredictable waves of financial services compliance requirements. Learn more about our work in financial services or sign up to attend a live demo.
Anti-Money Laundering (AML) fines and settlements are on the rise. For the ten years from 2009 to 2018, monetary settlements for non-compliance with AML and related regulations such as Know Your Customer (KYC) totaled $27B — certainly nothing to sneeze at. But in 2019 and 2020, there were $21.8B in fines, over 80% of the total from the previous decade in just two years. Of the $13.7B in fines for 2020, three of the largest U.S. banks accounted for over half of the total.
AML and related regulations present a tremendous risk to a financial institution monetarily through fines and lost business. While the average consumer might not care, the bad publicity for supporting money laundering can cost banks lucrative relationships with businesses, which is the heart of where they make their money.
The burden of AML compliance requirements and reporting.
Consolidated efforts with the creation of client and legal entity data utilities.
Invest in the right big data platforms and analytical tools.
Data Discovery and Analytic Data Pipelines
The move to outcomes-based compliance has been driven in part by the fact that bad actors are avoiding detection by strategically “following the rules.” For example, with currency transaction reporting required for all transactions above $10,000, perpetrators try to stay under the radar by limiting their transactions to just below this threshold. This tactic, known as smurfing, illustrates why banks need to go beyond traditional rules-based detection to proactively identify patterns indicating when customers circumvent the rules.
Effectively meeting all of these new compliance requirements is just part of the challenge facing financial institutions. It’s also difficult to manage the conflicting pressures of managing compliance breaches while controlling regulatory compliance costs. The tendency within banks is to “throw more bodies at the problem.” But this just drives up costs and leaves too much room for error.
To meet new AML requirements and expectations, most banks face a series of analytic challenges. AML teams typically have outdated analytic infrastructure, as financial institutions have been unwilling to invest in this area. But with the increasing risk of large fines, this is changing. And, with new regulations continuing to come out, including the new U.S. Anti-Money Laundering Act of 2020, AML budgets continue to get stretched thin.
Investment in big data analytic platforms dramatically increases the efficiency and effectiveness of existing AML staff. And as a result, they eliminate the need to throw more people at the problem, even as staff work with larger and more complex data sets. New AML requirements demand analysis of a wide variety of sources and types of data that encompass both public and private data sets in a variety of formats – structured, semi-structured, or unstructured.