The Impact of AI on the Anti-Money Laundering Sector

Salvatore LaScala is a Managing Director at Navigant Consulting, where he is Co-Lead of the Global Investigation and Compliance and Anti-money Laundering (AML) Practices. Mr. LaScala has over 20 years experience conducing AML and Sanctions compliance programme reviews, Risk Assessments, Monitorships and Remediations and regularly assists his financial services clients with Navigating regulatory or law […]

Salvatore LaScala is a Managing Director at Navigant Consulting, where he is Co-Lead of the Global Investigation and Compliance and Anti-money Laundering (AML) Practices. Mr. LaScala has over 20 years experience conducing AML and Sanctions compliance programme reviews, Risk Assessments, Monitorships and Remediations and regularly assists his financial services clients with Navigating regulatory or law enforcement actions. Mr. LaScala also applies his expertise by assisting clients with AML & Sanctions optimisation services that increases the breadth and scope of risk coverage while making the programme more efficient. He oversees Navigant’s AML Technology Team and has helped develop STAR, Navigant’s proprietary Case Management System and Rules engine regularly utilised for AML Look-Backs, Sanctions Look-Backs, CDD Remediations and other compliance and investigative projects. Additionally, Mr. LaScala provides his clients with outsourced Financial Investigation Unit (FIU) teams to both augment existing FIUs on a permanent basis or by providing FIU Surge protection services whereby the Navigant team is deployed to handle an increase in investigative or compliance activity pursuant to a compliance technology transformation or acquisition of another institution or large scale customer on-boarding.

Mr. LaScala began his career as an accountant, attorney and Special Agent with the IRS Criminal Investigations Division of the Treasury Department and thereafter spent over 20 years providing AML compliance and investigative services. He has been with Navigant since 2010.

This month, Finance Monthly had the pleasure to connect with Mr. LaScala and discuss AML in the US and the impact that AI, Machine Learning and Robotics Process Automation have had on the sector.

 

What drew to the AML field? What excites you about the sector you work within?

My background initially drew me in. As an accountant, attorney and former law enforcement officer, it all came together initially with a consulting job in 1997 with a Big 4 firm specialising in AML and Forensic Accounting. I enjoyed both but spent far more time in AML. I loved developing and dispositioning AML and Sanctions alerts and constantly found ways to make the process more comprehensive and efficient. Eventually I developed ways to make large scale AML remediations, including Look-Backs more efficient by building rules engines, false positive review platforms and custom case management systems. My perspectives as an accountant, attorney and former law enforcement officer helped make these technologies, auditable, regulatorily responsive and feature rich for investigators, respectively. These days I am still excited to be involved because I like working with clients, and because the regulations, financial institutions, and money launderers constantly change. It’s constant learning, which works for me – otherwise I’d be bored.

What is the current state of AML in the US?

This is a very important time for AML compliance – regulators, examiners and law enforcement now know more about AML programmes, compliance technology and payment platforms than ever before, and as such, the stakes for financial institutions regarding compliance become increasingly higher. Financial institutions are quickly adapting and upgrading their technology and overall programmes to maintain compliance and prevent and detect money laundering, terrorist financing and fraud. The ‘bad guys’ however, seem to have far more payment methods and venues at their disposal to commit crimes than ever before in history.

What are some of the key challenges you face on a daily basis and how do you overcome them?

The key challenges include finding innovative and cost effective ways to serve our clients, who are often faced with fines and expensive remediations. Providing the right breadth and depth of services to them in a cost effective way is critical. We also work for financial institutions of all different shapes and sizes, some have been through enforcement actions two or more times and are in a position to better plan their way through those actions with a great appreciation of the effort it takes. Others have either not been through too many regulatory or law enforcement actions, or are unable to communicate to a home office in a foreign country the gravity of a US regulator or law enforcement action, and don’t get the financial support they need to get through it. The challenge in both instances still becomes handling ongoing work or “business as usual work” (BAU) along with regulatory action or some compliance technology transformation. Without consultants helping, there are just not enough hours in the day. Regardless of a financial institution’s capacity to respond to a regulatory action, it’s often best if we get in there early and get them off to a timely start so they don’t also fall behind on BAU, or react to regulators too slowly, which can lead to additional issues.

What are the current AML issues and solutions affecting American businesses?

AML is constantly undergoing transformations. Some of these are based on new and emerging AML and Fraud schemes that the industry has to respond to, other transformations are due to new regulations, such as NYSDFS Part 504 regulations, which add additional layers of accountability on AML programme owners. Still, other transformations are the result of enhancing the regulations and the technology behind it because every time we close a door on money launderers and fraudsters, they both seek out institutions without robust compliance and find new venues through which to launder money. The US and several other markets are attractive to money launderers, fraudsters and terrorists because the financial services industry is vast and because these markets are segmented. This means that some players in capital markets or money service business spaces are very technologically savvy with respect to compliance, while other smaller players in the same segment are not. In fact, we often see challenges where the larger and more sophisticated financial institutions de-market or close customer/client accounts which later pop up at smaller or less sophisticated financial institutions.

How has the introduction of Artificial Intelligence, Machine Learning and Robotics Process Automation impacted compliance and investigative solutions?

Navigant is highly focused on applying Artificial Intelligence (AI), a form of Machine Learning (ML) and Robotics Process Automation (RPA) to our clients in many different areas, including AML and Sanctions. For AML example, we believe that AI/ ML can help existing AML Transaction Monitoring Systems deliver enhanced detection scenario parameters by grouping behavioural patterning to cover more risk and produce fewer false positives. Concurrently, we are applying RPA to the expedite portions of the dispositions of such alerts by removing mundane rote tasks from the analysts purview so that he/she is spending more time on considering the facts, CDD, news and current transactions to determine whether the transaction should be filed on, and less time hunting for data and writing the disposition. Specifically, AI/ML, which helps increase coverage and reduce false positives, and RPA which provides the Investigator more time to analyse the actionable items, are remarkably powerful together.That said, there is a fair amount of work to do, and in the beginning, we need to focus AI/ML only on matters for which the data feed is clean and comprehensive and apply it in a way that is transparent and can readily be described to regulators, examiners and internal audit. The AI/ML revolution won’t survive if the providers that developed it and the financial institutions that use it are not completely transparent. Moreover, even RPA will be better received if it is introduced in stages and when implementations are accompanied by statistically valid data showing that it is more accurate, and saves time such that the ultimate work product contains more thoughtful analyses and is generating comprehensive filings useful to law enforcement.

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