APRIMERTOMACHINELEARNINGFORFRAUDMANAGEMENT

TABLEOFCONTENTS

GrowingNeedforReal-TimeFraudIdentification...................................................................3

MachineLearningToday........................................................................................................4

BigDataMakesAlgorithmsMoreAccurate............................................................................5

MachineLearningForFraudPrevention.................................................................................5

ApplyingMachineLearning....................................................................................................6

MachineLearningEngines......................................................................................................7

BeyondFraudPrevention.......................................................................................................8

LimitationsWithMachineLearning........................................................................................8

ThePromiseofMachineLearningforFraudPrevention..........................................................8

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3

GROWINGNEEDFORREAL-TIMEFRAUDIDENTIFICATION

Fraudattacksaregettingtobemoresophisticated–astechnologyevolvesfraudstershaveelevatedtheirgameonpaymentfraudandmoneylaundering.Withaccesstofasterandcheapercomputing,fraudstershaveshiftedtheirtargetstomoreprofitableweakerpointsinthefinancialserviceschain.

Sixty-fivepercentoforganizationswithannualrevenuesofatleast$1billionwerevictimsofpaymentsfraudin2014comparedto56percentofcompaniesreportingannualrevenuesoflessthan$1billion.1

Newerbusinessmodelsareconstantlyevolving-frominstantdeliveryofgoodstovirtualcashtodigitaldownloads.However,thegrowthinopportunitieshasledtoacorrespondinggrowthinonlinefraudandfraudlossesparticularlyinecommercewhereitis7timesmoredifficulttopreventfraudthanintheperson2.AccordingtoLexisNexisFraudMultiplier,in2015,every$100offraudcostsamerchant$223intruecosts.

Theever-faster,ever-biggercycleofattacksleadstoanumberofconsequences:

MagnitudesofattacksareexponentiallyhigherFraudstersareemployingdistributednetworks,internalknowledge,bigdata,andevenmachinelearningtoeasilydetectvulnerabilityandmaximizethesizeoftheattacks

WeakestlinkscreatethemostexposureFinancialsystemsareinterconnectedandconsistofalongvaluechain,anetworkedecosystemofmultipleentitiesconnectingbuyersandsellers.Fraudflowstotheleast-protectedcomponents.

UnexpectedattackscanbeunsettlinganddisruptiveOrganizationscangofromnothavingafraudproblemtobeingdevastatedinjustafewdays(e.g.,Target,NeimanMarcus)

62%56%

65% 69%

55%

0%10%20%30%40%50%60%70%80%

All RevenueLessThan$1Billion

RevenueAtLeast$1Billion

RevenueAtLeast$1BillionandFewerThan26PaymentAccounts

RevenueAtLeast$1BillionandMoreThan100PaymentAccounts

PercentofOrganizationsSubjecttoAttemptedand/orActualPaymentsFraudin2014

CONCLUSIONFraudsolutionsneedstomoresophisticatedtokeepinpacewiththefraudstersandreactwithintheshorttimefraudattackshappentowhentheyarediscovered.Organizationsthatwanttodefendthemselvesagainstfraudneedtohaveasuperior,faster-learningsolutionthatcanconstantlyevolveyetiseasytouseandmaintain.

4

MACHINELEARNINGTODAY

Machinelearningasadatasciencetouncoverpatternsandhiddeninsightsisnotentirelyanewconcept–Ithasbeeninplaywiththeuseofneuralnetworksstartinginthe1980’s.Thequestionthereforeis,“Whyisthereabigbuzzaroundmachinelearningtoday?”

Theanswerliesinthefactthatadvancementintechnologyandsciencehasenabledgame-changingdifferencesinhowmachine-learningalgorithmshaveevolvedandisbeingapplied.

Forexample,traditionally,human-generatedrulesetswerethemostprevalentapproachinfraudmanagementandstillcontinuetobeinpracticetoday.Butthequantumleapincomputingpowerandavailabilityofbigdataoverthelast5yearshasdisruptedhowdataisbeingusedtoidentifyandpreventfraud.Machinelearningusesartificiallyintelligentcomputersystemstoautonomouslylearn,predict,actandexplainwithoutbeingexplicitlyprogrammed.Simplyput,machinelearningeliminatestheuseofpreprogrammedrulesets-nomatterhowcomplex.

Machinelearningenables:

Real-timedecisionsAdvanceswithin-memory,eventstreamingtechnologyallowriskscoringanddecisionmakinginthesub-secondrange(i.e.,ultra-lowlatency).

BigDatasetprocessingAdvancesindistributeddataprocessingallowanalyzingmoredatawhilestillmaintainingreal-timedecisionswithouttrade-offsbetweendataandlatency.

ReducedcycletimeLearningcyclesarecontinuousunlikebatchlearningwheremodelsbecomeout-of-date;Withmachinelearning,thesametransactionsbeingscoredalsoupdate/teachthemachinelearningmodels.

IncreasedeffectivenessExtremelysubtlepatternsandvariationscanbedetectedanddelivered(e.g.precision,recall)betterthanhumansinmanytasks.

Error-freeprocessingEnormousamountsofdatacannowbeprocessedwithouthuman-biasorerror.

CostefficienciesAddresslongtail“cornercase”distribution.

CONCLUSIONApplicationofmachinelearninghasredefinedpreviousstrategiesandtoolsinfraudmanagementdeliveringbenefitsthatwerepreviouslynotpossiblewithtraditionalmethods.

5

BIGDATAMAKESALGORITHMSMOREACCURATE

AsbusinessescontinuestoevolveandmigratetotheInternetandasmodernmoneyistransactedelectronicallyinanever-growingcashlessbankingeconomy,commerceisincreasinglybecomingthebusinessofbigdatascience.Ofthe$11TinUSpersonalconsumptionexpendituresprojectedin2017,anastounding79%ofthatwillbeintheformofelectronicpaymentswithafacevalueof$8.5T,ornearly50%oftheGDPoftheUS3.

Fortunately,thisrapidlyexpanding“dataverse”alsofuelsmodernartificialintelligence,makingbigdataaninextricablecomponentoftoday’sfraudmanagement.JustlikeIBM’sDeepBluecomputeroutplayedGarryKasparovbyhavinglearnedfrommillionsofchessgames,machinelearningingeneralrequiresaccesstolargeamountsofdatatobeabletolearnandgeneralizeknowledge.

Withoutlargeamountsofdata,amachine-learningalgorithmcannotlearn.Theexistenceofefficientalgorithmstoprocessthisdataveryquicklyopenedupthepossibilityforsophisticatedmachinelearningalgorithmssuchasspamdetection,efficientcontentrecommendations,autonomousdrivingcars,imagerecognition,naturallanguageprocessing,automatictranslation,andofcourse,fraudmanagement.

MACHINELEARNINGFORFRAUDPREVENTION

Tounderstandwhymachinelearningisimportantinfraudmanagement,weneedtounderstandthecharacteristicsoffraudalongwiththeassociatedbusinessandtechnicalchallenges.

Fraud’sUniqueCharacteristics:

FraudhasalongtaildistributionToomanyuniquecasestopursue.

FraudpatternschangequicklySlow-learningcountermeasurescannotkeepup.

FraudisadversarialProfessionalopponentsactivelyworkingtosubvertthesystemattheweakestpoints.

FraudmimicsgoodcustomerbehaviorsGoodcustomersarepenalizedbyover-intrusivecountermeasures.

MachineLearningdirectlyaddressesmanybusinesschallengesthataretimeconsumingandexpensive–ForExample:manualreviewsandfalsepositivesaloneaccountforalmost40%ofthetotalcostoffraudprevention.AccordingtoLexusNexus“TheTotalCostofFraudPrevention”study,merchantsallocateasmuchasone-fourthofcostsdedicatedtofraudpreventiontomanualreview.Furthermore,newcustomerchannels(e.g.,mobile,social),newproductsandbusinesslinespresentnewriskvectors-fraudthroughremotechannelsisupto7timesasdifficulttopreventasin-personfraud.

6

MachineLearningcan:

Reducemanualreviewqueuesthroughfastiteratingmachinemodels

Bechannel-agnostic

Easilyadapttonewbusinesslinesusingexperientialdata

Augmenthumandecision-makingwithincreasedprecision

Reducefalsepositiveswithbehavioranalysis

APPLYINGMACHINELEARNING

MachineLearningmodelscanbeusedtoveryefficientlyperformanalyticsanddeliverriskscoresinreal-time,withgreateraccuracybyleveraginglargeamountsofuserdata.Feedzai’sexistingmodelwasabletodetect+60%ofallfraudtransactionsforamajorretailercorrespondingto+70%oftheirfraudmoney.Whentrainedtoincludetheretailerfraud,themodelimprovedtodetect+65%offraudtransactionsand+75%ofthetotalfraudmoney.

Behavioranalyticsbuilddigitalfootprintswhichcanthenbeusedtolearnfrompastdatainordertomakepredictionsonfuture,unseendatapatterns.Forexample,inaretailenvironment,intelligencearounduserbehaviorcanbeusedtodeterminetheirbuyingschema–merchandisetheybuy,storestheyfrequentlyvisit,timestheyshop,channelthroughwhichtheyshopetc.,Machinelearningalgorithmscanthensynthesizethisdatacollectedfrommultiplesources–onlineandoffline-tobaselinebehaviorprofiles.Userattributesandotherdatafieldsusedbymachinelearningalgorithmscanautomaticallylearnpatternswhicharethenusedtomakepredictions.

Machinelearningcanalsobeusedtoautomaticallyderiveoutcomemeasurementssuchasastatisticalrisk(Themeasurementofthelikelihoodofincurringloss).Theeffectivenessofthestatisticalriskscoredependsonthemodel’sabilitytodetectanomaliesfromknownpatterns,identifymatchestoknownpatterns,anduncovernewpatterns.

CONCLUSIONSophisticatedmodelscanreverseengineermachinelogictopresenthuman-readablelanguagetoexplainmodeldecisions.

7

MACHINELEARNINGENGINES

Mathematicalalgorithmspowermachinelearning.But,thetruthisthereisnotonesinglebestalgorithmthatisuniversallybetterinallsituations-choosingthebestalgorithmdependsontheproblemtype,size,availableresources,etc.Havingsaidthat,RandomForests(akaEnsembleofDecisionTrees)andDeepLearninghavebeenshowntoperformverywellinanumberofscenarios,withSVM(SupportVectorMachines)aclosesecond.RandomForestsaremorerobustforanumberofrealworldproblemssuchasmissingdata,noise,outliers,anderrors.Inaddition,RandomForestsalsoallowmultipletypesofdata(numbersofdifferentscales,text,Booleans,etc.),canscaleverywell,parallelizeveryeasily,arefasttotrainandscore,andrequirelessefforttoachievethebestresults.ItisnosurprisethatRandomForestswinmanymachinelearningcompetitions(asdescribedbyKaggle.com,theworld’sleadingmachinelearningcompetitionsiteanddatasciencecommunity).

Algorithm Pro Con

RandomForest,akaEnsembleofDecisionTrees

•Generalizespatternswell•Robusttodifferentinputtypes(texts,numbersofscales,etc.)•Robusttomissingdata•Robusttooutliersanderrors•Fasttotrainandscore•Triviallyparallel•Requireslesstuning•Probabilisticoutput(i.e.ascore)•Canadjustthresholdtotradeoffbetweenprecisionandrecall•Verygoodpredictivepower•Foundtowinmanymachinelearningcompetitors

•Canbecomecomplextointerpretasnumberofdecisionsgrow(inherentnatureofincreasedcapacitytomakedecisions),butbetterthanallothers,especiallywithWhiteboxscoringtodemystifydecisionnodes•Requireslabeleddata

DeepLearning•Doesnotrequirelabeleddata•Reducesfeaturedesigntasks•Learnsmultiplelevelsofrepresentation(e.g.eyes,head,person)•Highlyparallel•Verygoodpredictivepower,especiallyintextandimageclassificationproblems

•Veryslowtrain,butbenefitsfromrecentarchitectureadvances(e.g.GPU’s,largeclusters)•Cannothandledifferentinputtypes•Needscalinginputs•Needstuning•Doesnotprovideprobabilityestimates•Lackofgoodinterpretability•Stillmissingtheoreticalfoundations

SupportVectorMachines(SVM)

•Abletodetectnon-linearandcomplexpatterns•Effectiveinveryhighdimensionalspaces•Verygoodpredictivepower

•Requireslabeleddata•Cannothandledifferentinputtypes•Needscalinginputs•Cannothandlemissingvalues•Notscalable•Slow•Needstuning•Doesnotprovideprobabilityestimates•Lackofinterpretability•Stillmissingtheoreticalfoundations

NeutralNetworks•Abletorepresentcomplexpatterns•Goodpredictivepower

•Requireslabeleddata•Cannothandledifferentinputtypes•Needscalinginputs•Cannothandlemissingvalues•Notscalable•Slow•Needstuning•Lackofinterpretability

K-NearestNeighbors

•Robusttomissingdata•Robusttooutliers•Goodpredictivepower

•Requireslabeleddata•Cannothandledifferentinputtypes•Needscalinginputs•Cannothandlemissingvalues•Needstuning•Lackofinterpretability

8

BEYONDFRAUDPREVENTION

Machinelearningisnotjustisolatedtoidentifyingandpreventingfraudinonlineretailenvironment.Machinelearningcanalsobeappliedwhereverlargeamountsofdatacanbeusedtounderstandandinferbehaviorforeffectivedecisionmaking.

• Accountopening:Validatetheauthencityofuserssigninguponlinetoverifyandacceptmoreapplicants• Paymentauthorization:Scorepaymentrequestsandauthorizepaymentsinreal-time• Checkoutscoring:Preventpaymentchargebacksbyscoringtransactionsduringcheckout• Merchantunderwriting:Protectyourmerchantportfoliothroughmerchantunderwriting• Marketplace:Maintaincommunitytrustbyconnectingbuyersandsellers

LIMITATIONSWITHMACHINELEARNING

OneofthebiggestobstaclestoMListhesteeplearningcurve.Datascienceknowledge,plustheamountoftimeanddataneededtocreatemodelsarebeyondreachofmanyriskteams.AsteeplearningcurvemeansdatascientistwhodomachinelearningneedtomastermanydifferenttoolssuchasR,Weka,Python,DBMS,NoSQLdatastores,Hadoopjobs,streamingsystemsandmore.Plus,itisveryhardtoevolveprofilesandmodelstoreflecttheever-changingnatureofbusiness,e.g.somecompaniesdeploy1-yearoldmodelsthatweretrainedusing2-yearolddata.

Thesecondbiggestchallengeisthatalotofmachinelearningisgroundedonblackboxdecision-making.Thisisaseriouslimitationasmanypolicyexecutionorgovernancerequirementsneedclearexplanationsofdecisions,e.g.explaintocustomerwhytransactionwasblocked.Finally,increasedcapacitytoprocessbigdatacreatesaninherenttendencytowardsincludeirrelevantdata.Machineslackcommonsensesohumansarestillneededtosupervise.

THEPROMISEOFMACHINELEARNINGFORFRAUDPREVENTION

Whilethemultiplemethodologiesinplacetodaytopreventfraudhavebeensuccessfulatkeepingfraudrateslowfortypicalpaymentfraud,theevolvinglandscapeofecommerceandmcommerceposenewerchallenges.Thesechallengesnecessitatemoreinnovativesolutionsthatcanrespondandreactquicklytofraud.Theneedforcomputationalpowertoprocesslargeamountsofdataandmakedecisionsrealtimeisimperativeforbusinessestoreachquicklytofraudattacks.Machinelearninginthisaspectisapromisingsciencethathaspotentialacrossmultipleenvironments.Frompaymentfraudtoabuse,machinelearningcaneasilyscaletomeetthedemandsofbigdatawithgreaterflexibilitythantraditionalmethods.

ABOUTFEEDZAIFeedzaiisfoundedondatascience,usingreal-time,machine-basedlearningtohelppaymentproviders,banksandretailerspreventfraudinomni-channelcommerce.Feedzaiwasdesignedfromthegroundupasabigdataanalyticsplatform,tunedspecificallyforthefraudmanagementdomain.

9

Source:12015AFPPaymentsFraudandControlSurvey2LexisNexusTruecostoffraudstudy20153http://www.nilsonreport.com/publication_chart_and_graphs_archive.php?1=1&year=2013,“PersonalConsumptionExpendituresintheU.S.”; USGDPin2012:$16.2T,http://data.worldbank.org/data-catalog/GDP-ranking-table).LexisNexus“TrueCostofFraud”2015study