Micro-jurisdictional risk: One of AML’s missing links

Editor's Note: This article originally appeared on The Asian Banker on May 9, 2017.

High level jurisdiction risk assessments alone are often too broad in scope to include in anti-money laundering policies; Micro-jurisdictional risk analysis could help allay model bias.

• Some aspects of AML policies and procedures are in need of an overhaul, to capture variables important to making accurate analysis
• Low risk jurisdictions have high-risk neighbourhoods and, conversely, not all customers and transactions from high-risk jurisdictions warrant heightened scrutiny
• Financial institutions should go beyond existing AML guidelines and apply a more granular risk-based approach to geography to stay ahead of upcoming best practices

Financial institutions tend to build their anti-money laundering (AML) frameworks based on regulatory guidelines and commonly accepted industry standards. This can include jurisdiction risk, a common input banks use when evaluating customers and their transaction activity to determine degrees of AML risk, or to identify suspicious activity. Jurisdiction risks are based on a number of factors, including links to sanctions, terrorism, narcotics, corruption and other legislative and government deficiencies. The problem with attempting to include jurisdiction risk in AML policies is that it is simply too broad in scope, ranging from entire countries to subcomponents of cities, which lends such analysis to be either overemphasised or underemphasised as a data input.

As an example, there is strong evidence from the US that crime often occurs in High Intensity Drug Trafficking Areas (HIDTA), given the link between violent crime, competing drug distributors, and addicts looking to finance their next fix. HIDTA is an example of a high-risk micro-jurisdiction within the US, and banks may factor this into their lending decisions.

By contrast, foreign micro-jurisdictions have generally not been a component of AML programmes because of the large number of geographic areas which would need to be monitored on an ongoing basis, and the difficulty of defining how a standard of a high-risk micro-jurisdiction could apply across the globe. This leaves a gaping hole in the AML process, which severely limits its potential global impact.

As an example, while Belgium is generally considered to be a country with low credit, compliance, and traveller risk, it has also proven to be a breeding ground for Islamic extremism with a 2016 report by the International Centre for Counter Terrorism in The Hague noting that an estimated 13% of EU foreign fighters in Syria have roots in Belgium - the greatest number per capita of any country in the EU. An issue for Belgium is that information is not consistently shared between law enforcement agencies, making it easy for leads to fall through the cracks. Brussels has six police forces, each reporting to a different mayor. Given that rudimentary intelligence information is such a difficult process, what real chance do national or global law enforcement authorities realistically have to apply AML standards to the transfer of funds between terrorism suspects in Belgium, and between Belgium and destinations in the Middle East?

Other high-risk micro-jurisdictions have been tied to terrorism. Evidence has shown that funding for the 1998 U.S. embassy bombing in Nairobi was through a hawala office in its infamous Eastleigh neighbourhood, an enclave for Somali immigrants. Kenya is generally considered to be high for risk for travellers, with Eastleigh being at elevated risk due to violent crime and terrorist activity. Another example is El Salvador, which became the world’s most violent country in 2016 with its capital, San Salvador, becoming the world’s most homicidal city. San Salvador is clearly another high-risk micro-jurisdiction where law enforcement has itself become a victim of the drugs and gang war, with 49 police officers killed in 2015 alone. High murder rates can imply greater AML risk in specific geographic areas.

Even though a correlation can undoubtedly be made between high-crime neighbourhoods and money laundering, it is worth asking whether financial institutions should be responsible for applying a risk-based approach to these areas, as they would be required to do for other, non-high risk jurisdictions. Clearly there is great need for stripping bias from decision making to enable managers to make better decisions about whether, and how, to apply AML methodologies. Placing greater significance on managing jurisdiction risk would appear to be an important way to create greater awareness among investigators who review customers and their transactions.

However, imagine two publicly traded multinational companies conducting transactions with one another from high-risk jurisdictions. That should automatically be considered high-risk by virtue of geography, because the nature of risk depends on the entities involved, their reputation risk, history, and potential AML exposure. Should there turn out to be a strong correlation between customer risk ratings and jurisdiction risk, it could be a symptom that jurisdiction risk is being overemphasised. The same bias could apply to an institution’s transaction monitoring system, which may generate alerts in proportion to the jurisdiction’s risk level they originate from or terminate in.

Model biases can manifest themselves in any number of areas within an AML programme, including customer due diligence, transaction monitoring and sanctions. Focusing on micro-jurisdictions and other factors linked to transactions can allow for the recalibration of existing models to adapt a more granular risk-based approach. While it may seem like a daunting task to identify high-risk micro-jurisdictions, data is available and can be made useful more easily than many institutions may imagine, with the right approach. It is incumbent upon all financial institutions to go beyond existing AML regulatory guidelines to find the most effective approach to AML. Eventually, the regulations will catch up with emerging best practices.

Keith Furst is founder and financial crimes technology expert of Data Derivatives. Daniel Wagner is managing director of Risk Cooperative and co-author of the book “Global Risk Agility and Decision Making”. The views expressed herein are strictly of the authors.