Anti-money laundering: using new analytics technology to uncover hidden criminal networks

Recent reports on leaked FinCEN Files shine a spotlight on weaknesses within the global anti-money laundering system once more. Part 1 of this article series presents the problem of how AML processes are failing due to inefficiencies across the entire ecosystem. Now in Part 2, learn how anti-money laundering analytics solutions help financial institutions better fight financial crime as industry specialists share their insight on the best tools and technologies for AML.

To expose and prevent money laundering activities, fast and efficient access to information is paramount. Suspicious Activity Reports (SARs) are a key piece of the puzzle and remain at the core of anti-money laundering programs because they provide intelligence that would otherwise not be disclosed.

How can financial institutions provide a full 360-degree view of clients and suspicious transactions reported in SARs to help law enforcement agencies more quickly investigate and neutralize criminal activities? In addition, what if authorities could identify connections between multiple SARs and better target investigation efforts by seeing the bigger picture? 

First, advancing public-private partnerships and the sharing of information between banks would open access to new data streams that are important for more thorough investigations. Initiatives such as the FinCEN Exchange in the US and the Joint Money Laundering Intelligence Taskforce in the UK are showing that progress is possible in that domain.

Next, financial institutions must commit to monitoring and reporting on high-risk suspicious activities more accurately and more comprehensively – which can only happen by better harnessing data. Specifically, banks need to focus on quality over quantity when it comes to SARs, aiming to deliver the most relevant, valuable information within a short timeframe.

Today's banking technology systems, including transaction monitoring and alerts systems, are largely outdated and lack flexibility. They struggle to meet demand. A recent report confirms that as much as 85% of financial-crimes compliance and AML activities in banks remain administrative or non analytic in character (1), such as the manual collection of data. 

Graph analytics technology is a powerful tool to enhance anti-money laundering activities. It allows financial institutions to process large amounts of data from various sources with speed and accuracy, which is otherwise impossible to carry out with legacy technologies. It can be combined both with legacy AML compliance tools, as well as with other technologies such as machine learning. 

Graph analytics thrives on high levels of complexity and uses advanced algorithms to connect the dots between multiple data points and sources to find suspicious connections. This makes it easier to detect both existing and new patterns of suspicious activity.

Graph technology can facilitate KYC processes like PEP or sanctions screening, or even identifying beneficial owners. It quickly shows you who you are doing business with and lets you pinpoint the information you need even in vast amounts of data from different sources. Because it gets to the heart of networks with speed and accuracy, graph analytics also helps reduce false positives.

For further insight, Miguel Aguado, AML expert and Senior Manager of the Financial Intelligence Unit at RIA Financial; and Miguel Fiandor, Data Specialist at the International Consortium of Investigative Journalists (ICIJ) speak from experience to offer solutions for modernizing anti-money laundering processes. 

Miguel Aguado, AML expert: “Emerging technologies play an important role in enhancing the fight against money laundering. Technologies that bring automation and proactivity are very valuable allies. From my point of view, machine learning and artificial intelligence today are still far from what we should expect of them; nevertheless we may see great results in coming years, once new technologies are developed further. A system that can adapt itself to an evolving environment can bring a huge positive impact to the AML monitoring process. Apart from that, graph technology is real and fully available. For this reason, it should be taken into account as an emerging technology (to adopt) now.”

Miguel Fiandor, Data Specialist: “A traditional system can only handle some complexity, and queries are too slow. New technologies help by letting you do both – for example, graph analytics display links and let you search for connections quickly. This data transparency is important to let banks know who is behind what.” 

Miguel Aguado, AML expert: “In my experience, graph technology is a great step forward in using the latest available technology for financial investigations. We use graph technology to perform linkage analysis, which has a wide implication on many different areas of the transaction analysis – from researching an individual customer to preventing large networks of possibly unusual customers being connected. As a result, this technology is largely used to prevent money laundering, fraud and terrorism financing. We have integrated graph technology in different steps of our analysis workflows. The most widely used one is for the set of alerts that are triggered when a group of customers are connected following a pattern, which has previously been defined as unusual. Moreover, we also use graphs when authorities highlight an individual potentially involved in crime. In this case, we detect all possible links with other individuals that may be related to them. Finally, we can obtain very illustrative visualizations for analysis performed with other tools in order to support SARs documentation. I would highlight two great enhancements since we started using this technology: Now we are able to identify patterns and ramifications of unusual transactions that were impossible to achieve with a traditional table-database approach. Furthermore, when we repeat an analysis executed before having graph technology, we find additional connections that we missed on the first analysis.” 

Miguel Fiandor, Data Specialist: “Complex data demands complex work, which is where graphs come in to find connections among players within the system. Graph technology is flexible and offers many benefits: it lets you search for links, find connections faster and collect data from multiple sources.” 

Miguel Aguado, AML expert: “It is attested that an obsolete AML monitoring system represents a significant risk for a financial institution. While financial supervisors have mostly quite modern monitoring systems, they cannot afford to fall behind. Another key thing to bear in mind is that criminal organizations are very creative in circumventing existing industry monitoring tools; consequently it is a regulatory and social obligation to remain effective and updated with the latest technology available.

Miguel Fiandor, Data Specialist: “Some banks have been fined in the past, so it is a risk to keep not doing things well. There is also a risk for not keeping up with competition: the world of finance changes constantly, and if a bank chooses to forget about technology, then they could be out of the game very fast.”

Detecting financial crime is highly complex, with incomplete and inconsistent information and ill-suited detection systems used as weaknesses to fly under the radar. However, as criminals get more sophisticated, adopting next-generation technology is a vital solution to modernize anti-money laundering resources. Armed with a new generation of graphics analytics technology, financial institutions can fight back faster and with more confidence to ensure that the most serious threats are dealt with effectively in line with their risk-based approach.