Banks are bringing artificial intelligence to the money laundering fight

While the battle against money laundering may seem perpetual, banks have advanced technology as a new weapon to fight it.

BAI Banking Strategies

Executive Report

April 2017

By Robert Stowe England


See full executive report at this link.


It’s an eternal worry for banks: how best to detect and report money laundering. And the problem grows bigger and costlier by the day. Such transactions represent 2 to 5 percent of global GDP—or roughly $1 trillion to $2 trillion a year, according to a PriceWaterhouseCoopers global survey of banks in 2015 and 2016.

While the battle against money laundering may seem perpetual, banks have advanced technology as a new weapon to fight it.

And to be sure, the burdensome endeavor to comply with anti-money laundering (AML) regulations finds banks fighting a battle on two fronts: to satisfy regulators and head off financial consequences (including big fines) before they occur. But financial services organizations now have a powerful new weapon at their disposal: artificial intelligence, or AI.

For some time, regulators have pressured institutions to employ more sophisticated technology to help battle money laundering, according to Julie Conroy, research director at Aite Group, a research and consulting organization based in Boston. Banks, for their own part, are driven to embrace new technology by a desire to ease the unrelenting cost pressures of compliance.

While a long overdue development, good reasons exist for the tech delays. “AML has always been, quite frankly, behind their anti-fraud counterparts in analytics,” she explains. This is due to the heavy emphasis regulators place on model risk management, especially in the U.S., Conroy points out.

But the tools are on their way. Since November 2016, IBM Corp. has applied the cognitive technology it developed with Watson to the task of complying with complex financial regulations. That’s when the company acquired the Promontory Financial Group a financial regulatory advisory company in Washington D.C. “So, this modern technology is just entering the marketplace right this minute,” says Gene Ludwig, founder and chief executive o icer of Promontory and former Comptroller of the Currency.

Watson is already proving the benefits of applying cognitive learning to AML compliance, according to Ludwig. It has some key advantages. First, he says, the tool is highly precise and can detect patterns that expert human monitors might occasionally miss.

“It just gets better and better,” says Ludwig.

Because the cognitive learning tool has so much computing power, it is faster and its cost savings are commensurately larger, according to Ludwig. The imperative, meanwhile, has never been greater.

The Clearing House Association in New York issued a report in February that calls for a new and more effective regulatory and enforcement paradigm for anti-money laundering—and for countering the financing of terrorism. The current state of affairs represents “a significant misallocation of resources away from activities that would be the most productive,” says Greg Baer, president of The Clearing House.

The report also notes that the AML compliance burden leads banks to de-risk by shying from customers who might pose more of an AML or terrorist-funding risk.

In the process, they push more accounts and activity out of the best-regulated banks, and o to institutions where they will no longer be properly monitored.

De-risking is driven in part by a fear of heavy fines. One problem is that “the burden of proof is really inverted in that you need to demonstrate to the regulators that there’s nothing wrong with this particular account or client you’re bringing into the bank,” says Don Andrews, partner and co-leader in compliance and risk management at the corporate group at Venable LLP in New York.

“Banks can lose a ton of money in fines and penalties if they are caught in situation where they allowed transactions to go through that had not be executed with the correct amount of due diligence,” says James McGovern, partner at Hogan Lovells in New York. McGovern is also a former chief of the Criminal Division of the U.S. Attorney’s O ice, Eastern District of New York, and deputy chief of the Business and Securities Fraud Sector.

In 2015, for example, more than 28 percent of enforcement actions issued by banking agencies against financial institutions were for AML and Bank Secrecy Act compliance, according to an analysis by Sullivan and Cromwell in New York.

And that’s not the only area where banks feel a bottom-line e ect. To deal with the ever-rising costs

of complying with AML regulations, banks by necessity must work diligently to improve the e ectiveness of their crime-fighting e orts.

Finding new ways to contain costs has not been easy. Fighting money laundering crime is a big and growing part of the banking business—and those costs are expected to mount at a cumulative rate of 2 percent a year. Projected through 2020, the jump could hit 10 percent overall: from $4.1 billion in 2015 to $4.5 billion, according to GlobalData plc, a data and insights solution provider based in London.

It’s also a labor-intensive enterprise and requires sophisticated monitoring capabilities. When banks

spot potential money laundering or fraud, they must file Suspicious Activity Reports and Currency Transaction Reports with Financial Crimes Enforcement Network (FinCEN), a bureau of the U.S. Treasury Department.

For a bank to perform its proper due diligence, it needs to “do deep dives” to get more information to build more support of any decision to accept the client. This extra e ort can be very costly, Andrews says. Meanwhile, a form of strict liability exists for the bank, in terms of fines from regulators, for failing to screen properly should something go wrong later.

But AI can alleviate some of those problems—and save money—by reducing the instances of false positives. That in turn reduces time wasted on bogus alerts and the number of mistaken Suspicious Activity Reports that a bank is required to file.

In the end, this increases the accuracy of filed reports, Ludwig says. And spending less time on false positives creates a true positive for banks: “It makes it easier to spot the bad guys.”

Robert Stowe England is a financial journalist who writes about retail and investment banking, financial markets and investing strategies. He is the author of five books, including “Black Box Casino: How Wall Street’s Risky Shadow Banking Crashed Global Finance.”

Copyright © Bank Administration Institute Banking Strategies




Robert Stowe England is an author and financial journalist who has specialized in writing about financial institutions, financial markets, retirement income issues, and the financial impact of population aging.

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