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Financial institutions and regulatory technology firms are leveraging artificial intelligence to bottle the flow of cash being funneled towards illegal activities worldwide.
An estimated $3.1 trillion in illicit funds passed through the global financial system last year, according to a Nasdaq’s latest Global Financial Crime Report. Money laundering alone accounted for trillions of dollars that helped fund international criminal activities, including $346.7 billion in human trafficking, $782.9 billion in drug trafficking, and $11.5 billion in terrorist financing.
Financial criminals are getting smarter and more dangerous with the help of advanced technologies that have become cheaper and easier to access than ever before. But financial institutions and RegTech companies are deploying many of the same technologies, including AI and generative AI, to help combat the growing criminal enterprise.
“What AI is allowing us to do is to really start seeing how bad actors are interacting with others,” said Nikhil Aggarwal, managing director in the Anti-Money Laundering Consulting practice at Deloitte Transactions and Business Analytics. “When you’re able to visualize a broader network, you’re able to do a deeper investigation into rings, and this really allows you to see some of those interconnected patterns in terms of how these threat actors are oftentimes working together.”
The U.S. Bank Secrecy Act was created in 1970 as a way to help financial institutions detect and prevent money laundering through their systems, also known as Anti-Money Laundering laws, or AML. Under the act, all financial institutions follow a set of guidelines known as KYC (Know Your Customer/Client) — a process that these firms use to verify the identity of, and risks from, potential clients.
Despite the regulation, financial crime has become more widespread with the rise of digital transactions, like online payments, withdrawals, and deposits. More than half of Americans use digital wallets more than their cards or cash, according to the results of a Forbes Advisor poll published last year.
This has created huge amounts of information on transactions and customer behavior — and that’s where AI comes in. RegTech firms use the technology to leverage the massive stores of data collected by banks to fight financial crime more efficiently and precisely.
“AI is good at analyzing larger scale of data and spotting patterns in a very large scale of data,” said Dagan Osovlansky, chief product officer at Israeli software company ThetaRay. “If you speak with any bankers, they will tell you they have tons of data and they don’t necessarily know how to use it in many cases.”
ThetaRay, which employs its own proprietary machine learning algorithms, takes a risk-based approach to targeting financial crime. Using a large swath of data points, the firm’s AI learns the normal behavior of banking customers in what’s known as “unsupervised learning,” a type of machine learning that learns from data without human oversight. This allows the technology to spot anomalies based on behavioral patterns, rather than human instruction.
The firm’s financial crime detection platform is used by over 100 financial institutions, including Santander, Payoneer, and Travelex. It alone monitors more than $15 trillion worth of transactions using AI. Last week, the company acquired Screena, a cloud-based, AI-powered screening firm that compares potential clients with lists of sanctioned parties. The partnership is part of efforts to keep up with advancing technologies that Osovlansky believes are being adopted by criminals faster than financial institutions.
“The real question, in my view, is, who’s winning? And I’m not sure the answer is the answer that we would like to hear,” Osovlansky said.
“I think we are playing catch-up,” he added.
Although its still its early stages, the use of this technology has already resulted in a significant decline in false positives — the flagging of normal banking activity as suspicious — at a number of ThetaRay’s partner banks, including in Santander’s corporate investment banking division, Osovlansky said. Santander has used ThetaRay’s anti-money laundering solution, which analyzes client data to detect anomalies that could indicate money laundering schemes, since the end of 2019.
Other players in the RegTech space include Lucinity, an AI software startup based in Iceland that uses AI to provide firms with insight to improve their financial crime compliance.
Some financial institutions, however, have their own in-house systems to use advanced technologies fight and improve their detection of financial crime. HSBC co-developed its AI system with Google to check for financial crime. The bank uses AI to monitor about 1.2 billion transactions for signs of financial crime across 40 million customer accounts each month, Jennifer Calvery, group head of financial crime risk and compliance at HSBC, wrote in a June blog post. It claimed to spot two to four times more financial crime than it did before, with 60% fewer false positives.
JPMorgan Chase chief operating officer Daniel Pinto said at the bank’s investor day in May that AI will make its KYC process, including customer onboarding and monitoring, up to 90% faster by the end of next year. That means processing 230,000 files with 20% less people. JPMorgan, the largest bank in the U.S., has been a leader in AI adoption within the banking world for years, with the highest volume of AI talent of all major global banks.
The biggest challenge for financial institutions and their partners is data availability. Underlying data blocks, data quality, and data hygiene are a “perennial challenge” when it comes to stringing data together to deploy AI effectively, according to Deloitte’s Aggarwal. The process of cleaning data, although time consuming and tedious, will give financial institutions and their RegTech partners more “powerful insights,” he said.
“I think this is where the opportunity is upstream to get some of the data fundamentals in place,” he said.