How to effectively detect money laundering in high-threat securities products
Knowledge around money laundering through securities is continuing to emerge, particularly as a result of recent schemes carried out at major European banks. However, some firms still struggle to identify their risk exposure and the complexity of securities products makes it difficult to effectively detect money laundering. By adopting a contextual approach to securities markets AML, you can better detect your risks, manage those risks more effectively, and drive down costs.
Anti-money laundering (AML) was the number one cause of FINRA fines in 2016, 2017 and 2018. As the number and value of enforcement actions increase worldwide, knowledge around money laundering through securities products is starting to emerge.
Unsurprisingly, a nagging challenge for most firms is detecting and assessing their money laundering risks and designing proper controls. Many firms are not aware of their risks – let alone the extent of these risks.
As a result, risk is often either overlooked completely or inadequately assessed within the firm’s overall risk and control framework.
Securities markets are often characterized by complexity, internationality, a high level of interaction, high volumes, speed and anonymity. However, some of the same characteristics associated with the sector can create opportunities for criminals.
Challenges of traditional detection technology for securities providers
A key driver of this deficiency is the difficulty in detecting money laundering typologies in securities products since the sector is most vulnerable to the integration stage of money laundering. Certain securities products have few transactions and often customers have a web of account relationships across a securities provider’s business units.
Yet many providers are still solely relying on traditional transaction monitoring systems, which are generating extremely high alert volumes and false-positive rates—often as high as 99 per cent—and keeping costs high. These systems are not rooting out critical areas of risk and specific typologies buried within complex customer networks.
As a result of this reliance on traditional monitoring systems, most institutions have taken a pre-determined, checklist approach to investigations. This process flies in the face of the complex schemes that criminals use to launder money through securities products.
Data quality also presents a key challenge. Often securities data is spread across a firm, specific fields can be uneven, and systems do not talk to each other. Even when data is available, it often requires a lot of organizing and normalizing to be useful for monitoring or investigations.
Contextual monitoring is changing AML processes
There is no one-size-fits-all approach for AML controls due to the complexity of the securities sector. FATF stressed the importance for securities providers to pursue a “group-level approach” to adequately mitigate money laundering and terrorist financing risks. A securities provider must consider its risks holistically across all its different business units. Then, it must design AML controls for its specific business profile, customer base, geographic footprint, and overall inherent risks.
Given these unique risks, securities providers must also evolve their monitoring and investigative approaches. The UK’s Financial Conduct Authority and U.S. regulators are encouraging technology innovation in AML and financial crime. Entity resolution and network analytics will prove critical to a successful risk-based, group–level approach.
By leveraging entity resolution and network generation, securities providers will be able to more precisely detect their risks to drive better SARs. These technologies also provide investigators quicker access to the intelligence necessary to conduct their investigations, resulting in significant cost savings as a result of decreases in time spent on each investigation and investigator headcount.
Those participants using network analysis had seen a vast reduction in the number of false positive alerts they received, when compared with using a traditional rules-based transaction-monitoring system.
What’s more, these solutions also will enable securities providers to more precisely model customer risk and money laundering typologies and help bridge the gap between trade surveillance and AML efforts. FINRA in its 2019 Report on Exam Findings explained that firms need to do a better job connecting the results of trade surveillance with AML suspicious activity monitoring. Fusing trade surveillance and AML efforts allows securities providers to build a more complete picture of the customer and their behaviours and relationships, resulting in higher quality, more complete Suspicious Activity Reports.
But securities providers cannot neglect the human side of this effort. As systems are re-tooled, investigators may need to be upskilled to transition from following pre-determined investigative steps to following all the intelligence available to them. Since securities products are highly complex, investigators must firmly understand the provider’s products and associated risk factors to effectively use the technology and make sound decisions.