Encouraged by a recent green light from regulators, the financial services industry is exploring new ways of using artificial intelligence (AI) to help them comply with banking regulations and to better detect fraudulent transactions used by criminals and terrorists.
This move toward new approaches to banking compliance comes despite growing concern that more government scrutiny could force the United States to fall behind similar efforts already underway overseas.
Last December, federal regulators, including the Federal Reserve System’s board of governors and the Federal Deposit Insurance Corporation, issued a joint statement encouraging the industry “to consider, evaluate and, where appropriate, responsibly implement innovative approaches” to detect money laundering operations and terrorist financing.
“The agencies realize that private sector innovation, including new ways of using existing technologies or adopting new technologies,” such as artificial intelligence, “can help banks identify and report money laundering, terrorist financing and other illicit financial activity by enhancing the effectiveness and efficiency” of their compliance programs, the regulators said.
The go-ahead resulted in a flurry of activity by banks, consulting firms, and fintech companies, all of which are seeking less expensive, more streamlined ways to monitor banking transactions for possible money laundering, the cost of which has risen to roughly $25 billion per year since new compliance requirements were put in place following the Sept. 11 terror attacks.
Tim Mueller, the managing director for global investigations and compliance at Guidehouse, a Chicago-based consulting firm, said the current enthusiasm reminds him of the late 1990s, when he was advising banks on how to enhance their businesses using the World Wide Web.
“Although you could make some pretty significant arguments that AI is going to be bigger than the internet,” said Mueller, who gave a presentation on Guidehouse’s efforts to harness the technology at the annual meeting of the World Economic Forum last month in Davos, Switzerland.
“It’s kind of impossible to ignore,” he added. “If you don’t start to understand how AI can help you serve your clients better, […] you’re going to be out of business and irrelevant in the very near future.”
Still, the industry is proceeding with caution. While banks and their partners feel emboldened by the December notice from regulators, they remain wary of its stipulation that banks may not be fully exempt from punishment if pilot programs expose existing gaps in their compliance operations.
In partnership with Ayasdi, a Silicon Valley-based machine-learning company that has also worked with Citibank and HSBC, Guidehouse is seeking to challenge the traditional means of weeding out possible money laundering when monitoring a large number of transactions, which Mueller described as “pretty dicey.”
Currently, transaction monitoring typically scrutinizes a limited data set and relies heavily on humans trained to spot red flags. Transactions are segmented by broad categories such as a client’s business type, location, or risk level as determined by the bank, which allows significant data to fall through the cracks.
The strategy tends to result in a high number of false positives — normal banking behavior initially flagged as suspicious. According to Mueller’s presentation at Davos, an estimated 95% of alerts generated in the first phase of a transaction review are found to be false positives, and 98% of alerts do not lead to the filing of a suspicious activity report.
Enter Ayasdi, which used artificial intelligence to mine four years’ worth of transaction data belonging to two of Guidehouse’s clients, Scotiabank of Canada and Intesa Sanpaolo of Italy, for instances of possible money laundering.
Instead of analyzing transactions using 20 or 30 categories, the banks were able to see data generated across 500 data points, said Alex Baghdjian, Ayasdi’s financial services strategy lead. False positives plummeted as a result, while the number of alerts that were chosen for further review rose.
“Not only were we able to get rid of the noise — all these alerts that were non-productive — but we also identified all these new areas of risk that were being escalated,” said Baghdjian. “Not only did we increase efficiency, but we drastically increased effectiveness as well.”
Guidehouse and Ayasdi aren’t alone in their pursuits. Bigger banks like WellsFargo are also experimenting with machine learning in the anti-money laundering sector. But firms are reluctant to charge too far ahead, lest they run afoul of regulators grappling with the promise and pitfalls of artificial intelligence.
“It’s not an industry that lends itself to being first movers,” said Mueller.
“It’s great that the regulators are putting these advisories out there,” he said. “But if we want to continue to be competitive within the financial services industry, our institutions can’t be at a disadvantage.”