Technology
Choosing AI to optimize AML compliance
Global Banking and Finance Review
Company
By Charmian Simmons
Financial institutions’ AML compliance functions today are under intense pressure to identify criminal activity more efficiently and effectively as they stop the flow of illicit money in the banking system. Increasingly sophisticated criminals, however, are getting harder to detect with traditional methods.
According to the United Nations, criminals annually launder as much as 5 percent of global GDP, or $2 trillion. The cost of financial crime compliance, meanwhile, reached almost $57 billion in 2022, a nearly 14 percent increase year-over-year. Fraud and related costs are also high and rising.
In this climate, investigators in banks, financial firms, and other institutions need to sift through more transactions more quickly than ever, while maintaining the best practices necessary to ensure proper due diligence. But humans alone can’t move fast enough given the pace of criminal activity, the volume of domestic and cross-border transactions, the changing regulatory landscape, and other factors that financial firms must consider.
This massive challenge is one reason why firms want more out of their compliance departments today. It’s also why the AML market is forecast to grow from $2.1 billion in 2021 to almost $8 billion by 2030, a CAGR of more than 15 percent, researchers found recently.
Optimizing how compliance teams manage AML alerts – speed to surfacing, relating, and giving meaning to data from internal and external sources to address alert cases – are standout advantages. They greatly reduce the cost of compliance, lessen the burden on resources and increase the detection of true financial crime.
In these uncertain times when conditions are perilous and challenges are ever-evolving, financial firms must optimize their AML compliance departments.
AI-driven compliance solutions are their most effective tool for identifying financial crime patterns and behaviors, weeding out false suspicious activity, and bringing together data in a more meaningful way for case investigations. These solutions make the most of people, processes, and technologies, helping investigators further leverage their skills to focus on real threats rather than false positives while maximizing resources and finding more crime.
AI and machine learning are capable and proven technologies that are already delivering the most trustworthy AML capabilities. Rapidly collecting and collating financial data, scanning it for known threats, and processing it accurately to identify potential new hazards, AI and machine learning-powered tools help humans make smarter judgment calls faster.
Case investigators, for instance, must assess alerts, deem them threats or not, pass them on to level 2/3 review where necessary, and take a host of other steps once they commence a Suspicious Activity Report (SAR). AI helps automate, organize, and streamline this time-intensive process. AI-augmented case investigators can then much more quickly and effectively pinpoint money laundering, fraud, and other crimes, cutting down on time spent searching for information in rafts of transactions and other burdensome steps.
The same process also monitors transactions over time to learn and anticipate future suspicious behavior, generating actionable intelligence for compliance departments that want to excel. These AI-led detection models draw on vast records of financial crimes and work in concert with investigators, learning from an institution’s workflows to better anticipate the next challenges and crime patterns.
Additionally, enterprise AI AML solutions can integrate new watch lists in as little as 15 minutes, configurable to any requirement, in real-time, and dynamically screen customers, employees, connected parties, entities, and transactions. Connecting disparate networks of cybercriminals, drawing links between behavior patterns, counter-party risk, and institutional risk exposure, they can then help executives determine whether to contact the authorities or offboard customers who might be dangerous.
Here’s a breakdown of how much the most innovative AI-driven AML solutions can benefit banks, financial firms, and others who need to be on the cutting edge of AML and financial crime prevention:
Money laundering, fraud, and other financial crimes are growing more prevalent and more ingenious. Disruptive global financial, economic, and technological trends are aiding and abetting bad actors. Innovations in AI are investigators’ best bet for leveling the playing field.
Charmian Simmons is a Financial Crime and Compliance Expert at SymphonyAI Sensa-NetReveal.
AML stands for Anti-Money Laundering, which refers to laws and regulations designed to prevent the illegal practice of generating income through criminal activities.
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems, enabling them to perform tasks such as learning and problem-solving.
A Suspicious Activity Report (SAR) is a document that financial institutions must file with authorities when they detect suspicious or potentially criminal activity.
Financial crime encompasses various illegal activities that involve deceit or fraud for financial gain, such as money laundering, fraud, and embezzlement.
Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on tasks through experience.
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