Increasingly, Hunting Money-Launderers Is Automated
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KEEN, no doubt, to stay alive, drug traffickers tend to be prompter payers than most. For software firms, this is just one of many clues that may hint at the laundering of ill-gotten money. Anti-money-laundering (AML) software, as it is called, monitors financial transactions and produces lists of the people most likely to be transferring the proceeds of crime.
Spending on this software is soaring. Celent, a research company, estimates that financial firms have spent roughly $825m on it so far this year, up from $675m last year. Technavio, another research firm, reckons the market is even bigger and will grow at more than 11% annually in coming years. This is partly because authorities are increasingly quick to punish institutions that let down their guard. Deutsche Bank, for example, has been hit with fines worth at least $827m this year alone. Governments, eager to appear tough on crime, are urging prosecutors to go after not just institutions, but also their employees.
The number of anti-laundering regulations is climbing yearly—by nearly 10% in America, Canada and the EU, and by roughly 15% in Australia, Hong Kong, Malaysia and Singapore, says Neil Katkov, a regulatory analyst at Celent. Even the red-tape-slashing administration of President Donald Trump is unlikely to cut regulation in this area.
David Stewart, head of anti-money-laundering systems at SAS, a software giant based in North Carolina, reckons that efforts to abide by such rules now take from a half to about 70% of most banks’ entire spending on compliance. A survey this year by Duff & Phelps, an advisory group, found that financial firms typically spend about 4% of revenue on compliance, a figure expected to reach 10% in 2022.
Many clues that lead software to block a transaction, or to flag it for a human to investigate, are straightforward. Round sums are more suspect than jagged ones. Spikes in transaction volumes and amounts are suspicious. So is cash deposited in an account via multiple branches. An area’s culture also matters. Sasi Mudigonda, of Oracle, says its software considers transactions linked to eastern Ukraine riskier than the west of the country, where Russian influence is weaker. Even age counts—crooks who move money disproportionately steal the identities of old people and young adults, says Michael Kent, chief executive of Azimo, a remittances firm.
Software also hunts for clues that someone on one of hundreds of watch lists has concocted a fake identity—the giveaway could be the opening of an account with a password or phone number once used by a corrupt official. ComplyAdvantage, a firm based in London, licenses software that generates long lists of suspected criminals by sifting through hundreds of millions of articles, including those in The Economist, and then determines which transactions may benefit one of them.
Moving the proceeds of big-ticket crime conventionally involves disguising them as legitimate trade payments. Software from a Singaporean firm, AML360, is designed to flag instances of this. Daniel Rogers, the company’s boss, says it monitors “a jigsaw puzzle” of factors such as ship itineraries, the locations of commodity producers and fluctuations in their prices. The software notices if a firm imports expensive stainless steel when a cheaper source of the material is closer at hand, say, or if an importer’s spending on copper rises as its price falls.
The next step for AML software is a big leap in the amount and types of data it crunches. Last year SAS launched Visual Investigator, developed at a cost of about $1bn. It links financial transactions with text and even imagery in reams of social media. This could reveal, for example, that a restaurant’s cash deposits appear too large for the amount of online “buzz” the business generates; or that a payment recipient skis with a kleptocrat.
With SAS software, rather more than half of flagged transactions lead to the filing of a suspicious-activity report (SAR) with authorities. Monique Melis, head of regulatory consulting at Duff & Phelps in London, argues that, to reduce “false positives” further, regulators should begin systematically to disclose the SARs that lead to a discovery of crime. Software could then be better calibrated to withstand a growing problem highlighted by Sophie Lagouanelle of FircoSoft, a Paris developer of AML technology: savvy launderers are learning how the software works to slip past it.
Should human analysts fear for their jobs? Probably not. They will still be needed to follow up on many flagged transactions. Business has not slowed for Berlin Risk, a German consultancy that discreetly investigates the nature of a person’s character and earnings by talking to as many as 20 people who know him. As its senior partner, Carsten Giersch, puts it, “You will never see a robot interviewing sources.” Or is that the next step?
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Author: The Economist
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