Microtheoretical arguments on anti-money laundering policy

Pleasecitethisarticleinpressas:McCarthy,K.J.,etal.,Modelingthemoneylaunderer:Microtheoreticalargumentsonanti-money launderingpolicy.InternationalReviewofLawandEconomics(2014),http://dx.doi.org/10.1016/j.irle.2014.04.006

ContentslistsavailableatScienceDirect

International Review of Law and Economics

Modeling the money launderer: Microtheoretical arguments on anti-money laundering policy

Killian J. McCarthy

^a,∗

, Peter van Santen

^b

, Ingo Fiedler

^c

aUniversityofGroningen,TheNetherlands

bSverigesRiksbank,Stockholm,Sweden

cUniversityofHamburg,Germany

a r t i c l e i n f o

Articlehistory:

Received22April2013

Receivedinrevisedform23January2014 Accepted14April2014

Availableonlinexxx

JELclassification:

E26 K42 H27 Keywords:

Moneylaundering Bargaining Deterrence

a b s t r a c t

Theexistingliteraturetreatsthecriminal–whogeneratescriminalproceeds–andthelaunderer–who convertsthe‘dirty’dollarsinto‘clean’ones–asoneandthesame.Andwithgoodreason:itisclear fromtheevidencethatsuch‘standard’verticallyintegratedlaunderersexists.Becauseprofessionalsand institutionsarealsoroutinelyprosecutedformoneylaunderingtransgressions,however,itappearsthat themarketformoneylaunderingisalsosuppliedbythirdparty,‘professional’launderers,whosecore businessliesoutsidethecriminalsector,butwhochoosestospendtimesupplyingthemarketformoney laundering.

Inthispaperweintroducetheprofessionallaundertotheliterature,andconsidertheprocessbywhich thelaundererandthecriminalbargain,toagreeonapriceforthemoneylaunderingservice.Wethen considertheeffectsofthreeanti-crime,oranti-moneylaunderingmeasures–namely,(1)increasing theprobabilitythatthecriminaliscaught,(2)increasingtheprobabilitythatthelaundereriscaught, and(3)increasingtheprobabilitythatthebargainingprocessitselfisdetected–onthewayinwhich thenegotiationisconcluded.Ofthevariouscombinationsavailabletothepolicymaker,weconclude thatmoreresourcesshouldbespendonspecializedpolice-unitstotacklemoneylaunderingand,when thebudgetisfixed,lessshouldbespentonfinancialscrutiny.Currentapproaches,wefind,donotdeter moneylaunderersfromsupplyingthemarket,butsimplyincreasetheprofitabilityofmoneylaundering anddecreasetheprofitabilityoflegitimatebusiness.

©2014ElsevierInc.Allrightsreserved.

1. Introduction

Moneylaunderingistheprocessbywhichcriminalsattempt to“concealordisguisethenature,location,source,ownershipor control”oftheirill-gottengains.¹

∗Correspondingauthor.

E-mailaddress:k.j.mccarthy@rug.nl(K.J.McCarthy).

1SeeStagesoftheMoneyLaunderingProcess,AReporttoCongressinAccordance with§356(c)oftheUSAPATRIOTAct,December2002.WithintheEuropeanlegal frameworkthe:(1)conversionortransferofproperty;(2)theconcealmentordis- guiseofthetruenature,source,location,disposition,movement,rightswithrespect toproperty;or(3)theacquisition,possessionoruseofproperty,knowingthatsuch propertyisderivedfromcriminalactivity,areallactivitieswhich,whencommitted intentionally,areconsideredtobeactsofmoneylaundering.SeeCouncilDirective 91/308/EECof10June1991onpreventionoftheuseofthefinancialsystemforthe purposeofmoneylaundering.

Innocuousasitsounds,moneylaunderingissaidtohavethe potentialto“shaketheveryfoundationsofsociety”² foratleast tworeasons.First,andbydesign,moneylaunderingattemptsto subvertthe‘crime-stoppingefforts’ofgovernments,andenables crimetoremainprofitable(Gnutzmannetal.,2010).Crimemust betackled,however,notonlybecauseitis“wrong”,“deviant”,and

“injurious”(Ormerod,2005),butbecausetheproceedsitcreates typicallybenefittheindividuallessthantheydamagesociety;esti- matesfromtheUSplacethenetcostofcrimeintheregionof$1 trillionperannum(ReuterandTruman,2004).Researchsuggests thatasmuchas80%ofcriminalproceedsarelaundered(Unger, 2007),andhencewithoutmoneylaundering,crimewouldbedra- maticallylessprofitable,andthesupplyofcrimewouldsufferan

2Directive2005/60/ECoftheEuropeanParliamentandoftheCouncil,of26 October2005onthepreventionoftheuseofthefinancialsystemforthepurpose ofmoneylaunderingandterroristfinancing.

http://dx.doi.org/10.1016/j.irle.2014.04.006 0144-8188/©2014ElsevierInc.Allrightsreserved.

quence,moneylaunderingdamagestheeconomy,andundermines thestabilityofthestate.Moneylaunderingisnotonlysaidtomul- tiplycrime,buttoincreasecorruption,briberyandterrorism,to distortprices,consumption,savingandinvestmentrates,toimpact thedemandformoney,interestandexchangerates,aswellasthe availabilityof credit,todamagethesolvability andliquidity, as wellasthereputationandprofitabilityofthefinancialsector,and toendangerthecontinuanceofforeigndirectinvestment(Unger, 2007).

Sowhosuppliesthemarketformoneylaundering?The‘stan- dard’scenario,uponwhichtheliteraturehasbeenbuilt,suggests thatthecriminal–whogeneratesthecriminalrevenue–andthe launderer– who convertsthe ‘dirty’ dollarsinto ‘clean’ones – areoneandthesameindividual.Andwithgoodreason:money launderersarecriminalsandcriminalsoftenlaundermoney.The term‘moneylaunderers’,infact,wasoriginallyusedtodescribe thosecriminals–theMafia–who,inthe1920s,boughtlaundro- matsandotheroutwardlylegitimatebusinessestohidthesource oftheirillegitimateincomes(Unger,2007).Andindeedthemafia todayisknowntoremainheavilyinvestedinthemarketformoney laundering.³

But clearly, this is not the full story. ‘Standard’ launderers exist, but theevidence is thatthird party professionals, whose core business lies outside the criminal sector, also choose to spendtime supplying themarket formoney laundering. Aswe will see in later sections, recent US investigations have not only seen organized criminals charged withmoney laundering ,butalsodozensofprofessionalbankers,lawyers,executives,and directors,threerabbisandevenoneUScongressmen.Ifthe‘stan- dard’scenarioisofaverticallyintegratedcriminallaunderer,the

‘professional’scenariothatweaimtointroduceinthefirstpartof thispaperisofathirdpartyagent,towhomcriminaloutsource theirmoneylaunderingneeds.

Inthesecondpartofthispaper,wedevelopabargainingmodel, toanalyzethe wayin which thecriminal and theprofessional moneylaundererinteract.Wethenusethismodeltoexaminehow differentpolicyoptionsimpactthewillingnessofthe‘professional’

launderertolaunder.Weconsidermeasuresto:(1)increasethe probabilitythatthecriminaliscaught,thatis,increasingtheinvest- mentintraditionalcrimefighting;(2)increasetheprobabilitythat thelaundereriscaught,thatis,increasingtheinvestmentinanti- moneylaunderingpoliceunits;andto(3)increasetheprobability thatthebargainingprocessbetweenthecriminalandthelaunderer isdetected,thatis,increasingthelevelofscrutinyrequiredofthe banksandotherfinancialplayers.Ofthevariouspolicycombina- tionsavailabletotheregulator,weconcludethatmoreresources shouldbespendonspecialistpolice-units–whichdirectlytackle moneylaundering–and,whenthebudgetisfixed,fewerresources shouldbespentonfinancialscrutiny.InGermanywherethecosts offinancialscrutinytothebankingsectorcometoD775millionper year(IWConsult,2006),thesuggestionfromourmodelisthatthose fundswouldbetterbespentontraditionalpolicingefforts,aimed atcatchingandpunishingprofessionalmoneylaunderers.Thissug- gestioniscontrarytocurrentpolicy,butinlinewithTakáts(2009) who–usingaverydifferentapproach–alsocallsforareduction ontheburdenofthebankingsector.

Indoingso,thispapermakesanumberofcontributions.First, andbyintroducingthe‘professional’launderer,wecreateamore realisticandmorecompletepictureofthemarketformoneylaun- dering.Second,weshowthelimitationsofcurrentapproachesto tacklingmoneylaundering.Andfinally,andmostimportantly,by

3 A2013reportbyEuropolnotedthattheItalianMafiawas,however,‘going green’,andturningtowind-farmsandEUsubsidiestolaundertheirmoney.

ers,weinvitefurtherresearchonmeasuresthatmightallowfor amoretargetedandmore efficientapproach totacklingmoney laundering.

Thepaperproceedsasfollows.Inthenextsectionweconsider theexistingliteratureonmoneylaundering,andpresenttheevi- dencethat‘professional’,third-partymoneylaunderingaccountfor anon-negligibleshareofthemarket.InSection3wethenanalyze thewaysinwhichthecriminalmightinteractionwithsuchamoney launderers,usingabargainingmodel.Westartbybrieflyintroduc- ingRubinstein(1982)andMuthoo (1999)’sworkonbargaining models,andthenapplyittomoneylaunderinginmathematical terms.Beforeweconclude,wediscusstheresults,andtheimplica- tionsfromapolicyperspectiveinSection4,andgiveanoutlookto futureresearchonthistopic.Relatedliteratureisdiscussedinthe respectivesections.

2. ‘Standard’and‘professional’moneylaunderers 2.1. Themarketformoneylaundering

Crimeisinevitable(Gnutzmannetal.,2010):itistheconse- quenceofhumanambition,andtheflip-sideofanentrepreneurial spirit(Baumol,1990).

Inobservingthatsomecrimeisrationalandprofitmotivated, thelevelofcrime,however,canbereduced.Rationalindividuals choosetospendtimeearninganincomeinthelegitimateorcrim- inalsectors.Theychoosetoinvestincrimewhen thecostsand benefits of investing in thecriminal sector is found to bethe moreprofitable.Inthelegitimatesectorthecostsincludetaxes andcharges,while inthecriminalsectorthecoststraditionally includefines,damagesandphysicaldetention.Rationalindividuals canthereforebedeterredfrominvestingincrimeby,forexam- ple,increasingtheprobabilityofcapture,orthedurationandthe severityofthepunishment(Ehrlich,1973;BlumsteinandNagin, 1977;Wolpin, 1978; Cornwelland Trumbull,1994).Thisisthe centralsuggestionoftheliteraturethatbuildsuponBecker’s1968 contribution.

Distinguishingbetween‘clean’money–earnedlegitimately– and‘dirty’money–generatedinthecriminalsector–isanother way in which regulators can reduce the profitability of crime (Masciandaro,2005;Unger,2007).‘Clean’moneycanbeconsumed, convertedandinvested,while‘dirty’moneycanonlybeconsumed.

Dirtymoney,thus,hasfeweruses,anditisthereforeworthless.And so,bydistinguishingbetweencleananddirtymoney,regulatorscan encouragelegitimateactivityovercriminalactivity.

As an unintended consequence, however, distinguishing between‘clean’ and ‘dirty’ money creates a newmarket – the marketformoneylaundering–anda newcriminal:themoney launderer.Moneylaunderersarethoseindividualsthathelpcrimi- nalsto“concealordisguisethenature,location,source,ownership or control” of their ill-gotten gains”,⁴ and as such the money laundereristhelife-linethatpermitscrimetoremainprofitable.

Estimatessuggestthatasmuch80%ofcriminalproceeds–afigure estimatedbytheUNtobeintherangeofUS$1600billionUNODC (2011)worldwide – are thoughttopass themoney launderers desks on a annual basis (see Fig. 1). Identifying and under- standingthe money launderer,therefore, is central to tackling crime.

4SeeStagesoftheMoneyLaunderingProcess,AReporttoCongressinAccordance with§356(c)oftheUSAPATRIOTAct,December2002.

1. Criminal Production

4. White / Legitimate

Economy

3. Money Laundering Market 2. Money

Launderer

80% Transferred to Money Launderers

20% Consumed on Cash Goods

100% of the Criminal Transfer

90% of Criminal Proceeds Returned

10% the Criminal Transfer Retained as Fees and Invested 70% of Criminal

Proceeds Cleaned for Investment

Fig.1. Moneylaunderingmarket.NumberstakenfromUnger(2007).

2.2. Distinguishingbetween‘standard’and‘professional’money launderers

The‘standard’assumptionoftheexistingliteratureisthatcrim- inalorganizationsverticallyintegratethelaunderingprocess,and housethetwofunctions–thatis,crime‘production’andmoney laundering– inthesame agent.Andwithgood reason:money launderersare criminalsand criminals oftenlaundermoney. In fact,dozensoforganizedcriminalshavebeenconvictedofmoney launderingtransgressionsbyUSauthoritiessince2000.

In the ‘standard’ scenario there is, therefore, no market for money laundering outside of the criminal organizations. But this is far fromthe full story. Recent investigations have seen dozensofprofessionalbankers,lawyers,executives,anddirectors, threerabbisandevenoneUScongressmen,chargedwithmoney laundering.⁵Table1listsseveralexamples.Take,forinstance,the case of FranklineJurado, the Harvard-educated bankerwho, in 1996,pleadedguiltytolaundering$36millionforJoseSantacruz- Londono, the head of the Cali Columbian drugs cartel. Jurado designedandoperatedasophisticatedscheme–whichinvolved routingmoniesfromPanamatoanumberofEuropeanshellcom- panies,andbacktoColumbia,viamorethan100bankaccountsin 68countries–butJuradowasneverinvolvedinactuallygenerating criminalrevenues.Hewasafinancier,andassuch,quiteadifferent animaltothemoretraditionalcriminalSantacruz-Londono.⁶And inthisJuradoisfarfromunique.Attheinstitutionallevel,banking giantslikeLlyodsTSB(2009),ABN-Amro(2010),Barclays(2010), Wachovia(2010),StandardChartered(2012),CreditSuisse(2012), ING(2012),theVatican’sBank(2012)–officiallyknownastheInsti- tutefortheWorksofReligion–andHSBC(2012)haverecentlybeen accusedofindependentlylaunderingbillions;thelatterwasfound tohavelaunderedupto$15billion,including$7billionforMexican drugcartels.

5Seewww.irs.govforalistofrecentmoneylaunderinginvestigationsintheUS.

6Juradowassentencedtosevenandahalfyearsinprison,in1996,forhisservices.

JoseSantacruz-LondonoheadedtheCaliCartelwhich,foratime,wasthoughtto havesupplied70%oftheUnitedStatesand90%oftheEuropeancocainemarket.

Santacruz-Londonowaskilledin1996whileattemptingtofleethepolice

Thus,whileitisclearthatthe‘standard’verticallyintegrated launderersstillexists,itisjustasclearthatindividualsandinsti- tutionswhosecorebusinessliesoutsidethecriminalsectoroften spendtimesupplyingthemarketformoneylaundering.Theindi- vidualsandinstitutionsareresponsibleforanon-negligibleshare ofthetotalmarketformoneylaundering,andthus treatingthe criminal–whogeneratesthecriminalproceed–andthelaunderer –whoturnsdirtydollarsintocleanones–asoneandthesame individualcannotfullydescribethemarket.

Inthispaperweintroducethe‘professional’launderertothelit- eratureforthreereasons.First,becausethescenarioofprofessional launderers supplying the marketfor money launderingreflects reality:notallcriminalsarelaunderers,andnotalllaunderersare criminals.Andeveniftheliteraturedoesnotcurrentlyrecognise thisdistinction,policymakers,aswewillseeinthisnextsection, do.Second,becausetheprofessionallaundereristheweakestlink inthecriminalchain.Havinginvestedinthecriminalsector,neither acriminalnoraintegratedlaunderercanbeeasilydissuadedfrom laundering;bothfacesignificantsunkcosts.Aprofessionallaun- derer,however,whohasalegitimatejob,areputation,acareer, lower/nosunkcosts,andasetofoutsideopportunities,ismuch morelikelytobedissuadedfromlaundering.Take,forexample, thecaseofLucyEdwards–anexecutiveattheBankofNewYork– who,in2006,wasconvictedoflaundering$10billionfortheRus- sianMob,foracommissionof$1.8million.Havinggenerated$10 billion,theMobhasasignificantsunkcost,and–haditdecidedto launderitsownrevenues–isunlikelytorespondtosubtletweaks totheanti-moneylaunderingprovisions.Havingalegitimate,and awell-paidpositionattheBankofNewYork,however,Edwardsis morevulnerableand,moreeasilydiscouragedfromsupplyingthe marketformoneylaundering.Finally,wedistinguishbetweenthe

‘standard’and‘professional’launderers,becausedoingsoallows regulatorsamoretargetedandmoreefficientapproachtotackling moneylaundering.

2.3. Currentapproachestotacklingmoneylaundering

Even though the academic literature does not distinguish between‘standard’and‘professional’launderers,anti-crime/anti- money laundering policy makers do. And recognising that the

Individuals

1 PatrickRobertSimon,aDallaslawyer,wassentencedto24months,in2013,forconspiringtolaunder.

2 ShawnRice,aLasVegasrabbiandlawyer,wassentencedto98months,and3yearssupervisionin2012,afterbeingfoundguiltyoflaundering$1.3 millionofcriminalmonies,generatedfromtheftandforgery.

3 RobertGeorge,aBostonattorney,convictedin2012forhelpingclientstolaundercriminalproceeds.

4 MarcoManuelLuis,areal-estateagentfromSanDiego,wasconvictedin2012forhelpingclientstolaunder.

5 MordchaiFish,theprinciplerabbiofCongregationShevesAchim,inNewYork,andhisbrother,RabbiLavelSchwarts,Brooklyn,weresentencedin 2012for15and10countsofmoneylaundering,respectively.FishandSchwartsroutedcriminalproceedsthroughseriesofpurportedcharities,fora 10%commission.

6 BrianEads,aninvestorinIndiana,wassentencedto30monthsin2012formoneylaundering.

7 JessicaHarper,thenheadofonlinesecurityatLloydsBank,wasconvictedin2012ofmoneylaundering.

8 JerryJarret,acriminaldefinesattorney,wassentencedto37months,in2011,forlaunderingdrugsproceeds

9 LucyEdwards,anexecutiveattheBankofNewYork,andherhusband,PeterBerlin,wereconvictedin2006forlaundering$10billion,generatedby Russianmobsters,forafeeof$1.8million.

10 FranklineJurado,aHarvardtrainedeconomistandbankerpleadedguiltyin1996tolaundering$36milliononbehalfoftheColombiandrugslordJose Santacruz-Londono.

Institutions

1 HSBC,aBritishbank,admittedlaundering$15billionfromRussia,andotherhighriskregionsin2012,including$7billionfromMexico,andagreedto payafinetotheUSGovernmentafineof$1.9billion.

2 StandardChartered,aBritishbankaccusedoflaundering$250billionforIranandLybia,settledwithUSregulatorsin2012andagreedtopay$340 millionforviolatingUSanti-moneylaunderinglaws.

3 CreditSuisse,aSwissbankaccusedoflaunderinginIran,Lubya,Sudan,MyanmarandCuba,settledwithUSregulatorsin2012andagreedtopay$536 millionforviolatingUSmoney-launderinglaws.

4 INGBankNV,aDutchBank,agreedtopayafineof$619millionin2012,forlaunderingmoneyoriginatinginCubaandIran,inviolationofUS money-launderinglaws,andfordeletingtheevidence.

5 ABN-Amro,aDutchbank,settledwithUSregulatorsin2010andagreedtopayafineof$500millionforhelpingindividualsinIranandLybiato channelmoneythroughtheUS,overaten-yearperiod.

6 Barclays,aBritishbank,settledwiththeUSGovernmentin2010,andagreedtopayafineof$298million,forbreachingUSanti-moneylaundering lawsrelatingtoclientpaymentsinCuba,Sudan,andotherplaces.

7 Wachovia,aUSbank,settledwithUSregulatorsin2010andagreedtopayafineof$160millionforhavinglaundered$373billion–asumequivalent to1/3rdofMexico’sGDP–onbehalfofaMexicandrugscartel.

8 LlyodsTSBGroupagreedtopay$350millioninfinein2009forlaunderingIranianandSudanesefunds.

9 TheInstitutefortheWorksofReligions–theVatican’sBank–wasaccusedoflaundering$218million.EttoreGottieTedeschi,theHeadoftheBank, andaProfessorofEthicsattheUniversityofTurin,wasdismissedasHeadoftheVaticanbankin2012forhisroleinthescandal.

10 AmericanExpress,anAmericanmultinationalinvolvedinfinancialservices,agreedin2007topayUSRegulatorsafineof$65million,forfailingto adheretoUSanti-moneylaunderinglaws.

marketis composedof ‘criminals’, ‘standard’,and ‘professional’

launderers, anti-crime authorities have created three types of instruments:

1.Measurestodetercrime:(denotedbyp_Cinthenextsection) Thiscategoryincludesthosemeasuresthatincreasetheprob- abilitythatcriminalsarecaught,reducingthesupplyofcriminal proceeds.Thesearetypical‘toughoncrime,toughonthesources ofcrime’measures,astheBritishGovernmentonceputit:more police,and more resourcesfor police.These measurestarget boththe‘criminal’,whoproducesthecriminalproceeds,andthe

‘standard’launderer.Theyare,forexample,aimedatcatching criminals,likeJoseSantacruz-Londono.

2.Measurestodetermoney-launderers:(p_M)

Thiscategoryofpolicyinstrumentincludesthosemeasures thatdecrease theprobabilitythat thelaundereris willingto launder.Thesemeasuresareaimedatreducingthesupply of moneylaunderingservices,andare,assuch,supposedtodeter

‘professional’launderersfromsupplyingthemarketformoney laundering. By doing so, these measures increase the costs oflaundering,andreducetheprofitabilityofcrime.Investing inthiscategoryseesauthoritiesfunnelresourcestospecialist anti-moneylaunderingbodies.Theresultisthatmoremoney launderers are caught, fined, fired, ‘named-and-shamed’ and imprisoned.In otherwords,this setofmeasuresis aimedat deterringpeoplelikeFranklinJuradoandLucyEdwards.

3.Measurestodetectthemoneylaunderingprocess:(q)

Thiscategory,implicitly,recognisestheexistenceofthepro- fessionallaunderer,andisdesignedtocatchpeoplelikeFranklin Jurado,inthebusinessoflaunderingJoseSantacruz-Londono’s money.Here, however, theauthoritiesdo not investin anti- moneylaunderingmeasures,buttypicallyshifttheburdenof

tacklingcrime/ money launderingtotheprivate-sector. The law,forexample,requires banksandfinancial institutionsto scrutinizelargetransactions(thatis,transactionsinexcessof US$10,000intheUS,CA$10,000inCanada,andD 10,000inthe EU);failingtocomplymakesthebankitselfliableforanti-money laundering fines. We model this category as the probability thatthebargainingprocessbetweenthecriminalandthepro- fessionallaundererisdetected,increasingthecostsofmoney launderingand,consequently,loweringthecrimelevel.

Inallcases,theobjectiveofthepolicymakeristofightcrime asefficiently,andaseffectivelyaspossible,givenbudgetlimita- tions.Ofcourse,theoptimuminvestmentisachievedwherethe marginaleffectofadollarspentonthecrimelevelisidenticalfor eachcategory.Butwithoutknowingtheelasticitiesthisdoesnot helpmuch.Sowhichpolicyisthemosteffectiveindeterringprofes- sionalmoneylaunderers?Thisquestionhasnotyetbeenexplored bytheexistingliterature,butinthenextsectionweintroducea theoreticalmodelwhichexploresthewayinwhichthecriminal andtheprofessionalmoneylaundererinteract,andassuchallows ustoevaluatethedifferentpolicyoptions.

3. Interactingcriminalandprofessionallaunders 3.1. Bargainingtheory

We model theinteraction betweenthe ‘traditional’criminal and the‘professional’money launderer usinga traditional bar- gainingmodel.Indoingso,weintendtointroducethenotionof bargaining,inarelativelysimplesettingwhich,atthesametime, isflexibleenoughtobeincorporatedinlargermodelsofeconomic behavior, or tobeextended along several lines.We follow the

alternating-offersproceduresetoutinRubinstein(1982).Here,two playersbargainoverthedivisionofafixedsurplus.Duringanybar- gaininground,oneplayermakesanoffertosplitthesurplus,while theothercaneitheracceptsorrejectstheoffer.Incaseofrejection, thesecondplayercanmakeacounterofferinthenextbargaining round.Thenumberofroundsisinfinite,andthesurplusisdivisible.

Thissettingisbothageneralandplausibledescriptionofeconomic exchangebetweentworationalagents.⁷Weapplythissettingto themarketformoneylaundering,followingthedescriptionofthe alternating-offersproceduresetoutbyMuthoo(1999),towhom wereferforanexcellentoverviewofdifferentbargainingmodels.

3.2. Modelingtheinteractionbetweencriminalsandmoney launderers

Consider now two players.The first is the ordinary Becker- Ehrlichcriminal (playerC), and thesecond a money launderer (playerM).Botharerationalutilitymaximizers,andbothareopen todeterrence,bychangingtheparametersintheirutilityfunctions.

Theutilityfunctionsofbothagentsareinterdependentbytheprice thecriminalpaysforhisproceedingstobelaundered.

Wemodeltheinterdependencebetweenthecriminalandthe moneylaundererusingabargaininggame.Therelevantoutcome is thepricethelaunderercharges forlaundering thecriminal’s proceedingsfromcrime.Thispricecanbeviewedasapayment fromthe criminalto themoney launderer in exchangefor the moneylaunderingservice.Thesurplusisdenotedby,whichis ofcourseafunctionofthetimespentintheillegalsector;thatis, equalsthelaunderedmonetaryequivalentofcriminalproceedings.

Wefollowthealternating-offersproceduresetoutinRubinstein (1982).Thetimingofthegameisasfollows:

1.Thecriminalchoosesthelevelofactivityinthelegalandillegal sectors,l₁andl₂,

2.Withtheinvestmentincrimesunk,thecriminalcomestothe launderer.Themoneylaundererproposesapriceofx∈(0,)for hisservices.

3.The criminal can either accept the price, yielding him net rewards−x,orcanrejecttheofferandproposeanewprice y∈(0,)duringthenextbargaininground.

4.Themoneylaunderercanacceptthenewprice,orcanrejectand returntostep2.

The bargaining game endswhen an offer by either party is acceptedbytheotherparty.However,thereisapositiveproba- bility,q,thatthegameisdetectedbythemonitor.Ifthishappens, thebargaining gameends,and bothplayersareforced topaya fine.Forsimplicity,weabstractfromtimediscounting,⁸butnote

7Despitethesimplicityofthesetup,thesolutiontothistypeofbargainingprob- lemwasnotfullyunderstooduntilRubinstein(1982)’streatmentofthesubject,the reasonbeingthatanysplitofthesurplusisaNashequilibrium.Inparticular,the playercancommittorejectinganyofferlessthan,say,60%ofthesurplus,andto alwaysoffer40%totheopponent.Thebestresponsetosuchastrategyistooffer the60%immediately.Byvaryingthethresholds,anyoutcomecanthusbeaNash solution.Rubinsteinshowedthatmostofsuchstrategiesinvolveincrediblethreats, andthatonly1equilibriumisalsosubgame-perfectoncebargainingiscostlyfor atleastoneplayer.Supposethatthesurplusshrinksovertime,eitherinrealterms orduetodiscountingfutureutility,by1%eachround.Inthiscase,thecommitted playerisbetteroffacceptinganofferof59.5%inanyround,despitebeingcommitted torejectingofferslessthan60%ofthesurplus.Inotherwords,alwaysrejecting“too low”offerscannotbepartofanequilibriumstrategy.Extendingthislineofreason- ingleadstoauniquesubgame-perfectequilibriumwheretheoutcomedependson therelativecostsofdelayforbothplayers.

8Allowingfortimediscountingispossible,butwillnotchangethequalitative conclusionsofthegame.Ignoringdiscountingforgoestheopportunitytoinvestigate interactionsbetweentheriskofbreakdownanddiscounting.

Table2

Payoffsafterthelaunderer’sproposalisaccepted.

State Probability Payoffcriminal Payofflaunderer Breakdown q w1l^C₁−fC v1l^M₁ −fM

Agreement 1−q w1l^C₁+−x v1l^M₁ +x qistheprobabilitythatthebargainingprocessisdetected,w1isthelegalwagerate, l1istheleveloftheactivityinthelegalsector,fiisthefineforthelaundererand thecriminalincaseofdetection,istheamountofmoneytobelaundered,xisthe pricethecriminalpaystothelaundererforhisservices.

thatrejectingoffersiscostlyintermsofallowingthegametobe detected.⁹ Moreover,inthispaperweassumethatlaborsupply decisionsbybothplayersarepre-determined,whichrulesoutout- sideoptions.Thepayoffs,focusingonthecasewherethecriminal acceptsthelaunderer’sproposedprice,aredepictedinTable2.The othercase,whenthelaundereracceptsthecriminal’sproposal,is ofcourseamirrorimage.

Henceforth,wewillusethenotationb_itodenotetheutilityof playeri=C,Mwhenbargainingbreaksdown.

Supposeeachplayeralwaysrejectsanyoffermadetohim.Then, theutilitytothecriminalequals

qbC

∞

t=0

(1−q)^t=bC

andthemoneylaundererhasutilityofb_M.Sinceneveragreeing onanyoffer is afeasible strategy, thecriminaland themoney laundererwillreceivea utilityatleastasbigasbi,i=C,M.This outcomeisknownastheimpassepointofthebargaininggame.

Thus,x^∗_M,theequilibriumpayofftothemoneylaunderer,isatleast aslargeasv1l₁^M−fM=U_M⁻¹(bM).Moreover,theequilibriumpayoff needstosatisfy−x_M^∗ ≥U_C⁻¹(bC),forotherwisegainsfromcoop- erationwouldnotexist.Ofcourse,neveragreeingisnottheoptimal strategy.¹⁰Theuniquesubgame-perfectequilibrium(SPE),which isduetoRubinstein(1982),hasthepropertythatthecriminalis indifferentbetweenacceptingandrejectingthemoneylaunderer’s equilibriumoffer,andviceversa.Wecanformulatethisas U_C(−x^∗_M)=qb_C+(1−q)U_C(x^∗_C) (1a) UM(−x_C^∗)=qbM+(1−q)UM(x^∗_M) (1b)

x^∗_M≥U_M⁻¹(bM) (1c)

x^∗_C≥U_C⁻¹(bC) (1d)

−x^∗_M≥U_C⁻¹(bC) (1e) −x^∗_C≥U⁻_M¹(bM) (1f) Theintuitionbehindcondition(1a)isasfollows.Withproba- bilityq,bargainingbreaksdown,yieldingb_Ctothecriminal.With probability1−q,thecriminalhasthechancetorejectthemoney launderer’s offerx^∗_M,and topropose hisown equilibrium offer, yielding UC(x^∗_C).The bestoffer the launderercan propose is to makethecriminalindifferentbetweenacceptingtheofferx^∗_Mand theexpectedutilityofthecriminal’sequilibriumstrategy.Tosee this,notethat offeringmorewouldreducethelaunderer’sown utility,whileofferinglesswouldberejectedbythecriminal.In equilibrium,neitherplayerhastheincentivetodeviatefromtheir strategies,yieldingEqs.(1a)and(1b).Uniquenessofthisequilib- riumfollowsfromRubinstein(1982).

9Wethusassumethatthedetectionriskincreaseswithincreasingbargaining time.

10Theplayerscouldhavechosennottoplaythegameatall.

(1a)and(1b),weknowthattheequilibriumproposalsatisfies GM(xM)=UC(−xM)−qbC−(1−q)UC(−U_M⁻¹(qbM+(1−q)UM(xM)))

=0

ThefunctionGM(xM)hasthefollowingproperties(seeAppendix AA):

1.G_M()<0.

2.GM(U_M⁻¹(bM))>0.

3.BycontinuityoftheutilityfunctionsanduniquenessoftheSPE, G_MisstrictlydecreasingontheintervalxM∈(U_M⁻¹(bM),).

Therefore,theequilibriumoutcomesatisfiesU_M⁻¹(bM)<x^∗_M<− U_C⁻¹(bC). The same line of reasoning for the criminal yields U_C⁻¹(bC)<x^∗_C<−U_M⁻¹(bM).Thelaundererproposesfirst,leaving himwithapayoffofx_M^∗ whilethecriminalobtains−x^∗_M.Three characterizationsoftheequilibriumareimmediate:

1.Agreementisreachedduringthefirstroundofbargaining,andis thereforeefficient.Delayingagreementwouldshrinkthesurplus duetothepossibilityofbreakdown.

2.AsGMincreasesinbManddecreasesinbC,thebreakdownpoint matters:ceterisparibus,increasingthemoneylaunderer’slegal wageordecreasingthecriminal’slegalwageincreasesthelaun- derer’sshareofthesurplus.Hisbargainingstrengthincreasesas hehaslesstogainfromlaunderingifhislegalwageishigh,and viceversaforthecriminal.

3.Thelaundererhasafirst-moveradvantage:iftheutilityfunc- tionsandbreakdownpointsareidenticalbetweenbothplayers (so U_M=U_C=U and b_C=b_M=b), then both would offer the sameamountinequilibrium,x^∗_M=x^∗_C=x^∗.FromEq.(1a),we knowthatU(−x^*)=qb+(1−q)U(x^*).Asx^*>U⁻¹(b),thisimplies U(x^*)>bandthusU(−x^*)<U(x^*).Itfollowsthatx^*>−x^*,and thereforethefirstproposerobtainsthelargershare.Thisisintu- itive, sincethe only asymmetrybetweenplayers whentheir utilityfunctionsandbreakdownpointsarethesameistheorder ofproposals.

Theeffect ofthe probabilityof arrest ontheshare goingto eachplayerisanimportanttoolforanalyzingdeterrence.How- ever,thiseffectisnotstraightforwardtoobtain.Intuitively, this isbecauseanincreaseinqwilldecreasetheexpectedtimeuntil breakdownoccurs,andthereforewillincreasethepresentvalueof thebreakdownpayoff.Ontheotherhand,aplayerbecomesmore impatient,andimpatienceistypicallybadfortheshareobtained inequilibrium.Thesetwoforcesworkinopposingdirections,the firstincreasingtheshareofthesurplus,andtheseconddecreasing theshareofthesurplus.Mathematically,wecanfindtheeffectof qonxMusing

∂x_M

∂q =− ∂G/∂q

∂G/∂xM

As∂^G/∂^xM<0(seeproperty3above),thesignof∂^xM/∂^qcoin- cideswiththesignof∂G/∂q.Thisexpressionequals

∂G

∂q=UC(−U_M⁻¹(qb_M+(1−q)UM(xM)))−bC+(1−q)U_C(−U_M⁻¹(qb_M+(1−q)UM(xM)))

bM−UM(xM)

U_M (U_M⁻¹(qb_M+(1−q)UM(xM)))

Fornotationalconvenience,defineZ=U_M⁻¹(qbM+(1−q)UM(xM)) tobethelaunderer’sexpectedpayofffromthebargaininggame.

Thefirstline,U_C(−Z)−b_Cisobviouslypositive,whilethesecond lineisnegative,sincebM<UM(xM),andhencethesignisambiguous.

Somemoreinsightcanbederivedasfollows:

surpluswillbethelargerone,andthereforehismarginalutil- itywillbelowerthanthecriminal’smarginalutility.Therefore, presumably,U_C(−Z)/U_M (Z)>1.

2.Suppose that (1−q)U_C(−Z)/U_M(Z)≈1. Then,

∂^G/∂^q≈(U_C(−Z)−b_C)−(U_M(x_M)−b_M).

3.If b_Mb_C, then ∂^G/∂^q>0. Alternatively, if b_M b_C, then

∂G/∂q<0.

Undertheseassumptions,wehaveshownthatincreasingthe probabilityofdetection increasestheshareofthemoney laun- dererwhenhisbreakdownvalueishigh,butdecreaseshisshare ifhisbreakdownpayoffislow,relativetothebreakdownpayoffof thecriminal.Theintuitionbehindthisresultisnatural:withahigh breakdownpayoff,thelaundererhasrelativelylittletogainfrom thebargaininggame,whilethecriminalhasmuchtogain.Increas- ingtheprobabilityofdetectiononlyincreasestheimpactofthe breakdownpayoffsonthefinaloutcome,andhencethelaunderer isbetteroff.

Example:risk-neutralplayers.Supposethatbothplayersare riskneutral.ThenEqs.(1a)and(1b)havethefollowingsolution:

x^∗_M=bM+ 1

2−q(−bM−bC) (2a)

x^∗_C=bC+ 1

2−q(−bM−bC) (2b)

Forthisparticularutilityfunction,weconfirmthemainprop- erties characterized above: the share going to the launderer is increasing in his breakdown payoff, decreasing in the crim- inal’s breakdown payoff and increasing in the probability of breakdown,q.

3.3. Interpretationoftheresults

Ourmodel,whilebeingstylizedandabstract,canbeusedto understandthepayoffstobothcriminalsandlaunderers.Thepayoff tothelaunderer:

•increaseswhenhislegalwagerateincreases

Thefirstpredictionshowsthatbetteroutsideoptions,inthe senseofhavingahigherlegalwagerate,willincreasethebar- gainingpowerofthelaunderer.Theintuitionisthatbargaining powerishighestforthepartythathastheleasttogainfromthe exchange.Ifthelegalwagerateishigh,earningextramoniesby engaginginmoneylaunderingislessattractivethanifthewage ratewaslow.Inoursetup,thelaundererhastoweighttheben- efitsofextraincomeandthecostsofpotentiallybeingarrested andconvicted,andforegoinglegalincome.Clearly,higherlegal wageratestipthebalanceinfavorofstayingoutoflaundering activities.

•increaseswhenthecriminal’slegalwageratedecreases Thesecondpredictionismoreindirect:whatmattersforthe bargainingpowerof thelaundereris notjust thelevel ofhis wage rate,but his wage raterelative tothat of the criminal.

Afterall,thepartywiththehighestoutsideoptionhasthemost

bargainingpower,andwillobtainthehighestshareofthesurplus.

Decreasingthecriminal’swageratesimplyleadstolessbargain- ingpowerforthecriminal(amirrorimageofthefirstprediction) andhencemorepowertothelaunderer.

•mostlikelyincreaseswhentheprobabilityofdetectionincreases

Thelastpredictionisduetotheequilibriumofthebargaining model.Recallthatthebestofferthelaunderercanproposeisto makethecriminalindifferentbetweenacceptingtheoffer,and theexpectedutilityofthecriminal’sequilibriumstrategy.Thisis duetothefactthatofferingmorewouldreducethelaunderer’s ownutility,whileofferinglesswouldberejectedbythecrimi- nal.Theexpectedutilityofthecriminal’sequilibriumstrategyis lowwhenthedetectionprobabilityishigh.Hence,thelaunderer needstoofferless,andinequilibriumobtainsthehighestshare ofthesurplus.Thisismostclearlyshownintheexamplewith risk-neutralplayers,thoughtheprincipleremainsthesamewith risk-averseagents.

We have analyzed the equilibrium in a bargaining model betweenacriminalandamoneylaunderer.Wehavederivedthe shareofthesurplusgoingtothelaundererundergeneralutility functions,and havecharacterizedthedeterminants.In thenext section,wediscusstheimplicationsforpolicyofthesefindings 4. Discussion

4.1. Discussion

Theexistingliteraturetreatsthecriminal–whogeneratescrim- inalproceeds–andthelaunderer–whoconvertsthe‘dirty’dollars into‘clean’ones–asoneandthesame.

Inthispaperwedescribethereal-worldsituationinwhichthe moneylaundererisoftenathird-party,specialistindividual.We introducetheprofessionallaunderer,inSection2,andthenmodel thewaysinwhichthecriminalsandthelaundererinteract,inSec- tion3,toconsidertheeffectsofthreeanti-crime,oranti-money launderingmeasures–namely,increasingtheprobabilitythatthe criminaliscaught(pC),thatthelaundereriscaught(pM),andthat thebargainingprocessitselfisdetected(q)–onthewayinwhich thenegotiationisconcluded.Inallcases,wedocumentspillover effects:increasingpC reducesthesupplyforcrime,andthesup- plyofproceedstothelaunderer;increasingp_Mreducesthesupply ofmoney launderingservices,increasesthepriceofthemoney launderingwhich,inturn,reducestheprofitabilityofcrime;and increasingqincreases theshareof theproceedsthat gotothe launderer,andreducestheprofitabilityofcrimeintheprocess.

Ofthevariouscombinationsavailabletothepolicymaker,we findthatmoreresourcesshouldbespenton(pM),intheformof specializedpolice-unitstotacklemoneylaunderingand,whenthe budgetisfixed,lessshouldbespentonfinancialscrutiny(q).This, however,iscontrarytothecurrentpolicy,whereonlyveryfew resourcesarespentonp_M.AccordingtotheUNaslittleas0.1%to 0.3%ofmoney launderingisdetectedUNODC(2011).Underthe currentregime,themoneylaunderer’sexpectedfine,therefore,is nearlyzero,whiletheprofitsremainastronomical:launderersare thoughttoearn5–50%oftheUS$1600billionincriminalproceeds UNODC(2011)thatcrosstheirdeskonanannualbasis.

Currentanti-moneylaundering effortsare focusedonq:the governmentexternalises,oroutsources,itsanti-moneylaundering efforts,andthroughregulation,itplacestheburdenofdetection uponprivatefinancialinstitutions.Accordingtoanti-moneylaun- deringpractitioners,thisapproachworks:notificationsbybanks and financial institutionsare responsible formore than 90% of moneylaunderinginvestigations.¹¹Suchmeasures,however,come withsignificantcosts–forGermanbanksalone,thecostsofcom- pliancewithanti-moneylaunderingdirectivesisintheregionofD 775millionperyearIWConsult(2006)–andareofquestionable

11 Wethankthemoneylaunderingexpert,SebastianFiedler(notrelatedtothe thirdauthor),forthisnote.

0 200 400 600 800 1000 1200 1400 1600

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Filings in 000’s. Source: FinCEN

Fig.2.Defensivefilings.

efficiency.Takáts(2009)demonstrates,inanotherway,theinef- ficiencyofcurrentapproachestoburdeningthebanks.Heshows, formally,thatfiningbanksforfailingtoreportmoneylaundering hasencouragedbanksto‘filedefensively’(FinCEN,2005),which has,inturn,explodedthenumberofreportsthatregulatoryauthor- itieshavehadtoprocess.Fig.2plotsanoverviewofthenumberof SuspiciousActivityReports(SARs)filedwiththeFinancialCrimes EnforcementOffice(FinCEN)oftheUnitedStatesDepartmentof the Treasury,obtained fromthe FinCEN website, in theperiod 1996–2012,andshowsadistinctlyupwardtrendinthenumber offilings.Theresulthasnotonlybeenadilutionoftheinformation –aphenomenonthatTakátscomparesto‘theboywhocriedwolf’

–buthasledtootothemisreportingofotherwiseinnocentactivi- ties.Asanexampleofthelatter,Takáts(2009)reportshowtheWall StreetJournalcoveredthecaseofthefalselyreportedformerpres- identialcandidateandSenatemajorityleaderBobDole(TheWall StreetJournal,2004).Takátsconcludesthattheproblemofcrying wolfmightberemediedbyreducingthefinesleviedonbanksfor failingtoreportsuspicioustransactions.Thecorroboratingimplica- tionfromourmodelisthatthedetectionprobabilitywillnotreduce thelevelofmoneylaundering,butwillsimplyincreasetheprofit- abilityofmoneylaundering.Inotherwords,ratherthanreducing thesupplyofmoneylaunderingservices,qmayhavetheeffectof increasingthesupplyofmoneylaunderingservices.

ThatthegovernmentdoesnotinvestinpMisunderstandable butnotsensible:thereturn toinvestmentonp_M isnot directly observable,andtheeffectsofmoneylaundering–intermsofitsdis- tortionaryeffectonthepricesystem–seemdistanceandabstract.

Ourresults,however,suggestthattheauthoritiesmustinvestin p_M,becauseaninvestment(only)inqisfarfromefficient.Onthe basisofourfindings,we:(1)advocateanexpansioninthenum- berofanti-moneylaunderingspecialistsemployedbynationaland internationallevel;and(2)callforaenhancedcommunicationand collaborationbetweentheexistingintelligenceunits,forexample bysettingupaninternationaldatabasetoincreasetheeffectiveness ofthecurrentanti-moneylaunderingspecialists.

4.2. Futureresearch

Muchworkneedstobedoneintheeconomicsofmoneylaun- dering.Andfurthereffortsatthemodelingandestimationofthe moneylaunderingmarketaretobewelcomed.Ingeneral,wewould like tosee: (1) scholarsand practitioners adopt thedistinction betweencriminalsandlaunderersproposedinthepaper;(2)more empiricalapplicationsofthecurrenttheoreticalresearch,inpar- ticular,usingtheorytoidentifyandmeasuremoneylaunderingin

themodeldevelopedinthispaperenriched,andexpanded,soasto provideadeeperinsightinthebehaviorofthemoneylaunderer.

5. Conclusion

Inthispaperweidentifytheprofessionallaunderer,asaratio- nalcriminalagent,andconsidertheprocessbywhichthelaunderer andthecriminalbargaintoagreeonapriceforthemoneylaun- deringservice.Wethenconsidertheeffectsofthreeanti-crime, oranti-moneylaunderingmeasures–namely,(1)increasingthe probabilitythatthecriminaliscaught,(2)increasingtheprobabil- itythatthelaundereriscaught,and(3)increasingtheprobability thatthebargainingprocessitselfisdetected–onthewayinwhich thenegotiationisconcluded.Ofthevariouscombinationsavail- abletothepolicymaker,weconcludethatmoreresourcesshould bespendonspecializedpolice-unitstotacklemoneylaundering and,whenthebudgetisfixed,lessshouldbespentonfinancial scrutiny.Currentapproaches,wefind,donotdetermoneylaunder- ersfromsupplyingthemarket,butsimplyincreasetheprofitability ofmoneylaundering.Indoingso,ourcontributionistobefound inthefactthatbyintroducingthe‘professional’laundererwecre- ateamorerealisticpictureofthemarketformoneylaundering, butmoreimportantly,andbydistinguishingbetweenthe‘stan- dard’and‘professional’launderers,weinvitefurtherresearchon measuresthatmightallowsforamoretargetedandmoreefficient approachtotacklingmoneylaundering.

AppendixA. OnthefunctionGM

ThefunctionG_M(x_M)isdefinedas

GM(xM)=UC(−xM)−qb_C−(1−q)UC(−U⁻_M¹(qb_M+(1−q)UM(xM))) ForxM=x^∗_M(theequilibrium),wehaveseenthatG_M=0.Wecan derivethefollowingproperties:

GM()=UC(0)−qb_C−(1−q)UC(−U_M⁻¹(qb_M+(1−q)UM())) Note that EU^max_M ≡qbM+(1−q)UM() is the maximum expectedutilitythelaunderer couldpossiblyget,that is, when hegetstheentiresurplus.Then,U⁻_M¹(EU^max_M )denotesthecorre- spondingmonetarypayoff,whichissurelylessthanthesurplus, .Therefore,theaboveexpressionsimplifiesto

GM()=UC(0)−qb_C−(1−q)UC(−U_M⁻¹(EU^max_M ))

ForanyutilityfunctionthatsatisfiesUC(0)≤0,whichcanalways beensuredbyrescalingit,thisexpressionisnegative,thedesired result.

qb_M+(1−q)b_M=b_M.EvaluatingG_MatU_M (bM)give GM(U⁻_M¹(bM)) =UC(−U_M⁻¹(bM))−qb_C

−(1−q)UC(−U⁻_M¹(qb_M+(1−q)bM))

=UC(−U_M⁻¹(bM))−qb_C−(1−q)UC(−U_M⁻¹(bM))

=q(UC(−U_M⁻¹(bM))−bC)>0

Thelaststepfollowsfromconstraint(1e).

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