Wash­ing whiter:

In­creas­ingly, hunt­ing mon­ey­laun­der­ers is au­to­mated

The Ukrainian Week - - CONTENTS - © 2017 The Econ­o­mist News­pa­per Lim­ited. All rights re­served

Anti-money laun­der­ing cam­paigns are get­ting smarter across the world

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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