We all have carrots and sticks that motivate us. The same is true for money launderers.
For financial criminals, the carrots are opportunities. Those opportunities could come from new laws, emerging technologies or changes in consumer behaviors. In any of those cases, it’s the change that creates the opportunity for the criminal.
The sticks are, of course, law enforcement and its partners.
The carrots and the sticks that motivate money launderers have evolved rapidly in recent years. Financial technologies and seamless connections between banking networks have created new opportunities for criminals who would like to hide their money trails.
At the same time, regulators and law enforcement officials have developed newer, better strategies for investigating these crimes.
Until recently, financial institutions primarily relied on rules-based anti-money laundering (AML) systems to flag suspicious activity, whether that was a transaction over a certain threshold or money flowing into a specific high-risk country.
Money launderers have since learned how to work around rules-based AML. This is where artificial intelligence (AI) enters the picture.
In this article we describe how AI is being used to fortify financial institutions’ AML systems and foster a new, risk-based approach to AML.
The importance of AI in anti-money laundering
Anti-money laundering refers to all of the activities financial institutions undertake to achieve legal compliance and prevent criminals from washing illegal funds into the economy. Money laundering happens at a massive scale, so scalable technologies like AI are needed to reveal money trails.
AI helps financial institutions detect money laundering activities at the transactional level — even when criminals try to hide that activity by stealing peoples’ identities or hiring money mules to facilitate transactions.
AI is a key component in latest-generation AML systems because it’s the technology used in transaction monitoring, and because it automates many of the key processes of the AML system.
Nestled within the realm of AI are technologies like machine learning (ML) and deep learning, which let financial institutions understand the actual behaviors of their customers. This allows those institutions to build accurate profiles of what constitutes normal customer behaviors, and what behavior appears suspicious.
These are the technologies that create a more efficient, more scalable AML system.
How Featurespace can support AML functions with AI
Featurespace’s ARIC™ Risk Hub is built with those machine learning and deep learning technologies.
Our Adaptive Behavioral Analytics technology specifically gives financial institutions a complete view of risks across their customer portfolios, makes AML more efficient by reducing false positives and prioritizing alerts, and finds connections in the data that reveal unforeseen threats.
Our AML technology is useful across a variety of applications, including:
- In retail banking, where the efficiency ARIC™ creates can bring down the cost of compliance.
- In payment services, where Adaptive Behavioral Analytics provides a richer context for identifying the behaviors of money launderers.
- In insurance, where Adaptive Behavioral Analytics can reveal emerging methods of money laundering.
- In correspondent banking, where machine learning can help uncover transnational networks of financial crime.
A new approach to AML
In late 2020, PwC Canada National Financial Crime Practice Leader Ivan Zasarsky spoke with Araliya Sammé, Head of Financial Crime at Featurespace, and Annegret Funke, Financial Crime Senior Solutions Consultant at Featurespace, about the new paradigm AML teams are embracing.
Rather than a rules-based approach, a more robust risk-based approach has emerged, thanks in large part to the technologies described above.
To learn more, have a look at their webinar below:
You can view the full webinar here.
How have we helped businesses fortify their AML systems?
HSBC is one example of a financial institution that recognized AI’s capabilities in detecting and preventing money laundering through insurance channels.
A few years ago, HSBC’s insurance business partnered with Featurespace in an effort to make its transaction monitoring capabilities more efficient.
Our team delivered a cloud-based implementation of ARIC™ Risk Hub, which has since helped the bank reduce false positives, improve the quality of alerts and identify new risk scenarios.
How have we helped regulators get to grips with these innovations?
At the end of 2021, the U.S. Financial Crimes Enforcement Network (FinCEN) sent out a request for information from across the financial sector for ideas as to how it can modernize its AML regulations and better combat organized crime with tools like artificial intelligence.
Our team responded with four recommendations:
- Structure data so that financial institutions can more easily leverage new AML solutions.
- Facilitate better data sharing.
- Define the level of model explainability financial institutions should pursue.
- Update frameworks with specific language, not broad language that has a chilling effect on fincrime innovations.
Our view is that technology has a major role to play in fighting financial crime and making the world in general a safer place for consumers and financial institutions. This is the mission we work toward every day.
It’s within that orientation that we see AI continuing to play an important role in AML.