On April 18, the Financial Crimes Enforcement Network (FinCEN) announced that they have imposed their first-ever penalty on a peer-to-peer cryptocurrency exchange for violating AML regulations, among other violations.
AML and Financial Crime
Insights and guidance from ACA's team of experienced compliance and technology professionals.
Every anti-money laundering (AML) program should be reviewed periodically to confirm that the program is performing efficiently and effectively. Analytics can play a big role in this review by providing new insights that support evidence-based decision-making.
Michael Held, Executive Vice President of the Legal Group at the Federal Reserve Bank of New York, spoke at the 1LoD Summit in New York on April 2, 2019.
When it comes to anti-money laundering (AML) transaction monitoring, financial services firms are under more pressure than ever to prove that the approach they are taking is working. Regulators want to see obvious evidence that firms are generating the right level of suspicious activity reports (SARs) for their size, geography, and business types, usually in the form of statistics and reporting. In turn, boards and senior management teams are now demanding to see this same information to be sure the firm is meeting its compliance obligations. As a result, AML transaction monitoring analytics are in more demand than ever before.
The Three Lines of Defense Model has gained popularity as the de facto model for organizing governance, risk management and internal control roles and responsibilities since the Institute of Internal Auditors (IIA) published “The Three Lines of Defense in Effective Risk Management and Control,” position paper in 2013. The IIA recently announced that they would embark on a key project to refresh and update this document.
Analytical segmentation modeling (ASM) is one way to design an effective AML monitoring strategy through the development of a quality model to achieve segmentation. ASM involves combining customer or bank accounts with similar properties and transaction behavior to make it easy for banks to formulate risk signals based on their various classes of customers. This model sets threshold levels for the segments monitored by identifying patterns based on the groups of similar customers and/or accounts.
Technology is an essential weapon for financial services firms in the battle against anti-money laundering (AML). The Office of the Comptroller of the Currency (OCC) issued a joint statement with other regulators earlier this month encouraging financial institutions to try new and innovative ways of combating AML. Three types of technology – advanced analytics, software robots, and artificial intelligence (AI) – could help make it easier to detect and prevent money laundering, as well as comply with existing regulations and follow the guidance of this joint statement.
While the financial institutions have the desire to improve the data quality and availability, data governance is often driven by external regulations to implement a program to ensure requirements outlined in DFS Part 504 regulation are met.
The US Office of the Comptroller of the Currency (OCC) has indicated that it will be focusing on the effectiveness of anti-money laundering (AML) systems and controls after including the topic on its list of FY 2019 annual priorities. For OCC-regulated banks, this means exams will concentrate on how up-to-date AML and Bank Secrecy Act (BSA) programs are with evolving threats and new rules.
On May 11, 2018, broker-dealers and other financial institutions currently covered by the US Anti-Money Laundering (AML) regime will need to become compliant with FinCEN’s new customer due diligence (CDD) requirements.
Due to the nature of its business, trade finance is considered a high-risk product that is frequently used by individuals and criminal organizations to launder funds, conduct terrorist financing, and evade the sanctions, regulations, and restrictions of the Office of Foreign Assets Control ("OFAC