How should key AML developments in the past year factor into your organization’s or clients compliance program?
Enforcement actions and penalties for non-compliance with anti-money laundering (AML) regulations have been increasing. US regulators have historically been the toughest enforcers of AML rules, but their European counterparts have been closing the gap. This chapter describes some of the challenges these organisations may face in ensuring an effective and sustainable AML program. In this chapter for the GIR EMEA Investigations Review, FRA’s AML experts outline:
- Changes in the legislative environment in the last year;
- Emerging trends relating to trade-based money laundering and virtual currencies;
- Evolving methods making use of machine learning and data sharing; and
- Lessons learned from recent AML scandals and key elements that should be present in a robust AML program.
The EU has seen more rapid change and advancement in legislation than the US recently, albeit with varying levels of implementation.
A series of Anti-Money Laundering Directives (AMLDs) were passed between 1991 and 2019, the most recent including the fifth AMLD (5AMLD, effective 10 January 2020) and the sixth AMLD (6AMLD, effective 3 December 2020). Nonetheless, we have seen a number of high-profile scandals involving European banks. The EU would benefit from a centralised AML supervisor as it would allow them to not only address the pervasive cross-border elements to money laundering, but apply the same standards across the EU so money launderers do not look for weak spots to exploit.
Money launderers will seek out new methods when the old ones become harder to execute.
Trade-based money laundering is an example of how tightened regulation of the banking sector has caused a shift in activity towards the non-bank financial sector, non-financial businesses and professions. Virtual currencies are also increasingly used as a vehicle for money launderers, drawn to the increased anonymity they provide. However, constraints remain in terms of scale, liquidity and market value volatility.
Information sharing practices are now expanding beyond the authorities to include private-public and inter-bank arrangements, where legal mechanisms permit.
Among its many provisions, the 5AMLD called for UBO lists to be made public within 18 months, and also mandated functional public politically exposed persons (PEP) lists. Successful instances of public-private information sharing are also emerging across EMEA, though these require careful navigation of data privacy laws and require legal mechanisms to first be established.
Regulators have been encouraging the use of innovative approaches, such as artificial intelligence and machine learning to more effectively identify suspicious activity.
Monitoring customer activity to identify suspicious patterns or behaviour can only be achieved successfully when an institution effectively aggregates their data across systems, divisions and geographic locations. A number of financial institutions have reported positive findings from pilot programs leveraging advanced technologies. AML detection may often be automated, but is generally not predictive. For example, automated tools may be configured to identify suspicious transactions based on typical red flags, such as rapid, successive transfers of money. However, if a machine learning solution was used to analyse the totality of customer and transactional data, entities could begin to identify unusual patterns worth investigating before they become known red flags.
For more insight from FRA’s AML experts, catch our webinar on Strengthening your AML Compliance Program: AML Trends and Challenges.