賦能數(shù)字經(jīng)濟：人工智能在金融行業(yè)的機遇和風險

MONEYANDCAPITALMARKETSAND

INFORMATIONTECHNOLOGYDEPARTMENTS

PoweringtheDigital

Economy

OpportunitiesandRisksofArtificial

IntelligenceinFinance

PreparedbyElBachirBoukherouaaandGhiathShabsigh

incollaborationwith

KhaledAlAjmi,JoseDeodoro,AquilesFarias,EbruS.Iskender,

AlinT.Mirestean,andRangacharyRavikumar

DP/2021/024

2021

OCTOBER

MONEYANDCAPITALMARKETSANDINFORMATIONTECHNOLOGYDEPARTMENTS

DEPARTMENTALPAPERS

PoweringtheDigitalEconomy

OpportunitiesandRisksofArtificialIntelligencein

Finance

PreparedbyElBachirBoukherouaaandGhiathShabsigh

incollaborationwith

KhaledAlAjmi,JoseDeodoro,AquilesFarias,EbruS.Iskender,AlinT.Mirestean,andRangacharyRavikumar

Copyright?2021InternationalMonetaryFund

PoweringtheDigitalEconomy:OpportunitiesandRisksofArtificialIntelligenceinFinance

DP/2021/024

Authors:ElBachirBoukherouaaandGhiathShabsigh

incollaborationwith

KhaledAlAjmi,JoseDeodoro,AquilesFarias,EbruS.Iskender,AlinT.Mirestean,andRangacharyRavikumar

1

Cataloging-in-PublicationData

IMFLibrary

Names:Boukherouaa,ElBachir.|Shabsigh,Ghiath.|AlAjmi,Khaled.|Deodoro,Jose.|Farias,Aquiles.|Iskender,EbruS.|Mirestean,Alin.|Ravikumar,Rangachary.|InternationalMonetaryFund,publisher.

Title:Poweringthedigitaleconomy:opportunitiesandrisksofartificialintelligenceinfinance/preparedbyElBachirBoukherouaaandGhiathShabsighincollaborationwithKhaledAlAjmi,JoseDeodoro,AquilesFarias,EbruS.Iskender,AlinT.Mirestean,andRangacharyRavikumar.

Description:Washington,DC:InternationalMonetaryFund,2021.|2021September.|Departmentalpaperseries.|Includesbibliographicalreferences.

Identifiers:ISBN9781589063952(paper)

Subjects:LCSH:Artificialintelligence—Economicaspects.|Machinelearning—Economicaspects.|Financialservicesindustry—Technologicalinnovations.

Classification:LCCHC79.I55B682021

ISBN

978-1-59806-395-2(Paper)

JELClassificationNumbers:

C40,C510,C550,E17,G21,G23,G280,O310,O330

Keywords:

ArtificialIntelligence,MachineLearning,FinancialStability,EmbeddedBias,FinancialRegulation,Cybersecurity,RiskManagement,DataPrivacy

Author’sE-MailAddress:

GShabsigh@;EBoukherouaa@;KAlAjmi@;JDeodoro@;AFarias@;ESonbulIskender@;AMirestean@;RRavikumar@

TheDepartmentalPaperSeriespresentsresearchbyIMFstaffonissuesofbroadregionalorcross-countryinterest.Theviewsexpressedinthispaperarethoseoftheauthor(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,orIMFmanagement.

Publicationordersmaybeplacedonlineorthroughthemail:

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elibrary.IMF.org

1WearegratefultoAdityaNarainandotherIMFcolleaguesforvaluablecomments,andtoJavierChangforproductionsupport.

1IMFDEPARTMENTALPAPERSPoweringtheDigitalEconomy

ExecutiveSummary

Thispaperdiscussestheimpactoftherapidadoptionofartificialintelligence(AI)andmachinelearning(ML)inthefinancialsector.Ithighlightsthebenefitsthesetechnologiesbringintermsoffinancialdeepeningandefficiency,whileraisingconcernsaboutitspotentialinwideningthedigitaldividebetweenadvancedanddevelopingeconomies.Thepaperadvancesthediscussionontheimpactofthistechnologybydistillingandcategorizingtheuniquerisksthatitcouldposetotheintegrityandstabilityofthefinancialsystem,policychallenges,andpotentialregulatoryapproaches.Theevolvingnatureofthistechnologyanditsapplicationinfinancemeansthatthefullextentofitsstrengthsandweaknessesisyettobefullyunderstood.Giventheriskofunexpectedpitfalls,countrieswillneedtostrengthenprudentialoversight.

AIandMLaretechnologieswiththepotentialforenormoussocietalandeconomicimpact,bringingnewopportunitiesandbenefits.Recenttechnologicaladvancesincomputinganddatastoragepower,bigdata,andthedigitaleconomyarefacilitatingrapidAI/MLdeploymentinawiderangeofsectors,includingfinance.TheCOVID-19crisishasacceleratedtheadoptionofthesesystemsduetotheincreaseduseofdigitalchannels.

AI/MLsystemsarechangingthefinancialsectorlandscape.CompetitivepressuresarefuelingrapidadoptionofAI/MLinthefinancialsectorbyfacilitatinggainsinefficiencyandcostsavings,reshapingclientinterfaces,enhancingforecastingaccuracy,andimprovingriskmanagementandcompliance.AI/MLsystemsalsoofferthepotentialtostrengthenprudentialoversightandtoequipcentralbankswithnewtoolstopursuetheirmonetaryandmacroprudentialmandates.

Theseadvances,however,arecreatingnewconcernsarisingfromrisksinherentinthetechnologyanditsapplicationinthefinancialsector.Concernsincludeanumberofissues,suchasembeddedbiasinAI/MLsystems,theopaquenessoftheiroutcomes,andtheirrobustness(particularlywithrespecttocyberthreatsandprivacy).Furthermore,thetechnologyisbringingnewsourcesandtransmissionchannelsofsystemicrisks,includinggreaterhomogeneityinriskassessmentsandcreditdecisionsandrisinginterconnectednessthatcouldquicklyamplifyshocks.

AI/MLinfinanceshouldbebroadlywelcome,togetherwithpreparationstocapturetheirbenefitsandmitigatepotentialriskstothefinancialsystem’sintegrityandsafety.Preparationsincludestrengtheningthecapacityandmonitoringframeworksofoversightauthorities,engagingstakeholderstoidentifypossiblerisksandremedialregulatoryactions,updatingrelevantlegalandregulatory,andexpandingconsumereducation.ItisimportantthattheseactionsaretakeninthecontextofnationalAIstrategiesandinvolveallrelevantpublicandprivatebodies.

Cooperationandknowledgesharingattheregionalandinternationallevelisbecomingincreasinglyimportant.ThiswouldallowforthecoordinationofactionstosupportthesafedeploymentofAI/MLsystemsandthesharingofexperiencesandknowledge.Cooperationwillbeparticularlyimportanttoensurethatless-developedeconomiessharethebenefits.

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Contents

ExecutiveSummary1

AcronymsandAbbreviations

4

1.Introduction

5

2.ArtificialintelligenceintheFinancialSector

7

A.Forecasting

7

B.InvestmentandBankingServices

7

C.RiskandComplianceManagement

9

D.PrudentialSupervision

9

E.CentralBanking

12

3.RisksandPolicyConsiderations

14

A.EmbeddedBias

14

B.Unboxingthe“BlackBox”:ExplainabilityandComplexity

15

C.Cybersecurity

16

D.DataPrivacy

17

E.Robustness

17

F.ImpactonFinancialStability

18

4.Conclusion

20

Annexes

Annex1.HowMachineLearningAlgorithmsWork

21

Annex2.ArtificialIntelligenceinFinance—RiskProfile

24

Annex3.NationalArtificialIntelligenceStrategies

25

References

28

BOXES

Box1.ArtificialIntelligenceandMachineLearningCapabilities

6

Box2.ArtificialIntelligenceinInvestmentManagement—SampleUseCases

8

Box3.ArtificialIntelligenceinCreditUnderwriting

8

Box4.ArtificialIntelligenceinRegulatoryCompliance—SampleUseCases

10

Box5.ArtificialIntelligenceinSupervision—SampleApplications

11

Box6.ArtificialIntelligenceinCentralBanking—SampleApplications

13

Box7.Explainingthe"BlackBox"

16

FIGURES

Figure1.TopFiveTechnologiesEmployedinRegulatoryTechnologyOfferings

9

Figure2.TechnologiesUsedinSuprvisoryTechnologyTools

10

AnnexFigure1.1.MachineLearningParadigms

22

3IMFDEPARTMENTALPAPERSPoweringtheDigitalEconomy

AnnexFigure1.2.ExampleofanInputAttack

23

AnnexFigure3.1.NationalArtificialIntelligenceStrategyLandscape

25

AnnexFigure3.2.KeyFeaturesofNationalArtificialIntelligenceStrategies

26

4IMFDEPARTMENTALPAPERSPoweringtheDigitalEconomy

AcronymsandAbbreviations

AI

ArtificialIntelligence

AML/CFT

Anti-MoneyLaundering/CombatingtheFinancingofTerrorism

Fintech

FinancialTechnology

ML

MachineLearning

NLO

NaturalLanguageProcessing

OECD

OrganisationforEconomicCo-operationandDevelopment

Regtech

RegulatoryTechnology

Suptech

SupervisoryTechnology

5IMFDEPARTMENTALPAPERSPoweringtheDigitalEconomy

1.Introduction

Thispaperexplorestheuseofartificialintelligence(AI)andmachinelearning(ML)inthefinancialsectorandtheresultantpolicyimplications.

1

ItprovidesanontechnicalbackgroundontheevolutionandcapabilitiesofAI/MLsystems,theirdeploymentandusecasesinthefinancialsector,andthenewchallengestheypresenttofinancialsectorpolicymakers.

AI/MLsystemshavemademajoradvancesoverthepastdecade.Althoughthedevelopmentofamachinewiththecapacitytounderstandorlearnanyintellectualtaskthatahumanbeingperformsisnotwithinimmediategrasp,today’sAIsystemscanperformquitewelltasksthatarewelldefinedandnormallyrequirehumanintelligence.Thelearningprocess,acriticalcomponentofmostAIsystems,takestheformofML,whichreliesonmathematics,statistics,anddecisiontheory.AdvancesinMLandespeciallyindeeplearningalgorithmsareresponsibleformostoftherecentachievements,suchasself-drivingcars,digitalassistants,andfacialrecognition.

2

Thefinancialsector,ledbyfinancialtechnology(fintech)companies,hasbeenrapidlyincreasingtheuseofAI/MLsystems(Box1).Recentadoptionbythefinancialsectoroftechnologicaladvances,suchasbigdataandcloudcomputing,coupledwiththeexpansionofthedigitaleconomy,madetheeffectivedeploymentofAI/MLsystemspossible.Arecentsurveyoffinancialinstitutions(WEF2020)showsthat77percentofallrespondentsanticipatethatAIwillbeofhighorveryhighoverallimportancetotheirbusinesseswithintwoyears.McKinsey(2020a)estimatesthepotentialvalueofAIinthebankingsectortoreach$1trillion.

AI/MLcapabilitiesaretransformingthefinancialsector.

3

AI/MLsystemsarereshapingclientexperiences,includingcommunicationwithfinancialserviceproviders(forexample,chatbots),investing(forexample,robo-advisor),borrowing(forexample,automatedmortgageunderwriting),andidentityverification(forexample,imagerecognition).Theyarealsotransformingtheoperationsoffinancialinstitutions,providingsignificantcostsavingsbyautomatingprocesses,usingpredictiveanalyticsforbetterproductofferings,andprovidingmoreeffectiveriskandfraudmanagementprocessesandregulatorycompliance.Finally,AI/MLsystemsprovidecentralbanksandprudentialoversightauthoritieswithnewtoolstoimprovesystemicrisksurveillanceandstrengthenprudentialoversight.

TheCOVID-19pandemichasfurtherincreasedtheappetiteforAI/MLadoptioninthefinancialsector.BoE(2020)andMcKinsey(2020b)findthataconsiderablenumberoffinancialinstitutionsexpectAI/MLtoplayabiggerroleafterthepandemic.Keygrowthareasincludecustomerrelationshipandriskmanagement.BanksareexploringwaystoleveragetheirexperienceofusingAI/MLtohandlethehighvolumeofloanapplicationsduringthepandemictoimprovetheirunderwritingprocessandfrauddetection.Similarly,supervisorsrelyingonoff-siteintensivesupervisionactivitiesduringthepandemiccouldfurtherexploreAI/ML-supportedtoolsandprocessesinthepost-pandemicera.

TherapidprogressinAI/MLdevelopmentcoulddeepenthedigitaldividebetweenadvancedanddevelopingeconomies.AI/MLdeployment,andtheresultingbenefits,havebeenconcentratedlargelyinadvancedeconomiesandafewemergingmarkets.Thesetechnologiescouldalsobringsignificantbenefitstodevelopingeconomies,includingenhancedaccesstocreditbyreducingthecostofcreditriskassessments,particularlyincountriesthatdonothaveanestablishedcreditregistry(Syandothers2019).However,theseeconomiesarefallingbehind,lacking

1FollowingtheOxfordDictionary,AIisdefinedasthetheoryanddevelopmentofsystemsabletoperformintellectualtasksthatusuallyrequirehumanintelligence.MListhelearningcomponentofanAIsystem,andisdefinedastheprocessthatusesexperience,algorithms,andsomeperformancecriteriontogetbetteratperformingaspecifiedtask.GiventhatAIandMLheavilyoverlapandthatmoststatementsinthispaperholdtrueforbothconcepts,thetermsareoftenusedasapair(AI/ML).

2SeeAnnex1formoredetails.

3Thisincludesrevenuegainsandcostsavings.

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thenecessaryinvestment,accesstoresearch,andhumancapital.

4

Bridgingthisgapwillrequiredevelopingadigital-friendlypolicyframeworkanchoredaroundfourbroadpolicypillars:investingininfrastructure;investinginpoliciesforasupportivebusinessenvironment;investinginskills;andinvestinginriskmanagementframeworks(IMF2020).

Cooperationamongcountriesandbetweentheprivateandpublicsectorscouldhelpmitigatetheriskofawideningdigitaldivide.Sofar,globalinitiatives—includingthedevelopmentofprinciplestomitigateethicalrisksassociatedwithAI(UNESCO2021;OECD2019),callsforcooperationoninvestingindigitalinfrastructure(see,forexample,GoogleandInternationalFinanceCorporation(2020)),andtheprovisionofaccesstoresearchinlow-incomecountries(see,forexample,AI4G)—havebeenlimited.Multilateralorganizationscouldplayanimportantroleintransferringknowledge,raisinginvestments,buildingcapacity,andfacilitatingapeer-learningapproachtoguidedigitalpolicyeffortsindevelopingeconomies.Similarly,themembershipinseveralintergovernmentalworkinggroupsonAI(suchastheGlobalPartnershiponArtificialIntelligenceandtheOECDNetworkofExpertsonAI,amongothers)couldbeexpandedtoincludeless-developedeconomies.

AI/MLadoptioninthefinancialsectorisbringingnewuniquerisksandchallengesthatneedtobeaddressedtoensurefinancialstability.AI/ML-baseddecisionsmadebyfinancialinstitutionsmaynotbeeasilyexplainableandcouldpotentiallybebiased.AI/MLadoptionbringsinnewuniquecyberrisksandprivacyconcerns.FinancialstabilityissuescouldalsoarisewithrespecttotherobustnessoftheAI/MLalgorithmsinthefaceofstructuralshiftsandincreasedinterconnectednessthroughwidespreadrelianceonfewAI/MLserviceproviders.Chapter2explorestheadoptionofAI/MLinthefinancialsectorandpossibleassociatedrisks,Chapter3discussesrelatedpolicyconcerns,andChapter4providessomeconclusions.

Box1.ArtificialIntelligenceandMachineLearningCapabilities

?Forecasting.Machinelearningalgorithmsareusedforforecastingandbenefitfromusinglargedatasets.Theyusuallyperformbetterthantraditionalstatisticaloreconometricmodels.1Inthefinancialsector,thisisusedinsuchareasascreditriskscoring,economicandfinancialvariablesforecasting,riskmanagement,andsoon.

?Naturallanguageprocessing.Artificialintelligencesystemscancommunicatebyunderstandingandgeneratinghumanlanguage.Boostedbydeeplearningandstatisticalmodels,naturallanguageprocessinghasbeenusedinthefinancialsectorinsuchapplicationsaschatbots,contractreviewing,andreportgeneration.

?Imagerecognition.Facialandsignaturerecognitionisbeingusedbysomefinancialinstitutionsandfinancialtechnologycompaniestoassistwithcarryingoutcertainanti-moneylaundering/combatingthefinancingofterrorism(AML/CFT)requirements(forexample,theidentificationandverificationofcustomersforcustomerduediligenceprocess),andforstrengtheningsystemssecurity.

?Anomalydetection.Classificationalgorithmscanbeappliedtodetectrareitems,outliers,oranomalousdata.Inthefinancialsector,insidertrading,creditcardandinsurancefrauddetection,andAML/CFTaresomeoftheapplicationsthatleveragethiscapability(Chandola,Banerjee,andKumar2009).

4SeeAlonsoandothers(2020)forabroaderdiscussionaboutpossibleimplicationsofAIondevelopingeconomies.Inparticular,thepaperfindsthatthenewtechnologyriskswideningthegapbetweenrichandpoorcountriesbyshiftingmoreinvestmenttoadvancedeconomieswhereautomationisalreadyestablished,withnegativeconsequencesforjobsindevelopingeconomies.

7IMFDEPARTMENTALPAPERSPoweringtheDigitalEconomy

2.ArtificialIntelligenceintheFinancialSector

Thecapabilityofacquiringlargesetsofdatafromtheenvironmentandprocessingitwithartificialintelligence(AI)andmachinelearning(ML)ischangingthefinancialsectorlandscape.AI/MLfacilitatesenhancedcapacitytopredicteconomic,financial,andriskevents;reshapefinancialmarkets;improveriskmanagementandcompliance;strengthenprudentialoversight;andequipcentralbankswithnewtoolstopursuetheirmonetaryandmacroprudentialmandates.

A.Forecasting

AI/MLsystemsareusedinthefinancialsectortoforecastmacro-economicandfinancialvariables,meetcustomerdemands,providepaymentcapacity,andmonitorbusinessconditions.AI/MLmodelsofferflexibilitycomparedtotraditionalstatisticalandeconometricmodels,canhelpexploreotherwisehard-to-detectrelationshipsbetweenvariables,andamplifythetoolkitsusedbyinstitutions.EvidencesuggeststhatMLmethodsoftenoutperformlinearregression-basedmethodsinforecastaccuracyandrobustness(BolhuisandRayner2020).

WhiletheuseofAI/MLinforecastingoffersbenefits,italsoposeschallenges.Useofnontraditionaldata(forexample,socialmediadata,browsinghistory,andlocationdata)inAI/MLcouldbebeneficialinfindingnewrelationshipsbetweenvariables.Similarly,byusingAInaturallanguageprocessing(NLP),unstructureddata(forexample,theinformationinemailtexts)canbebroughtintotheforecastingprocess.However,theuseofnontraditionaldatainfinancialforecastingraisesseveralconcerns,includingthegoverninglegalandregulatoryframework;ethicalandprivacyimplications;anddataqualityintermsofcleanliness,accuracy,relevancy,andpotentialbiases.

B.InvestmentandBankingServices

Inthefinancialsector,advancesinAI/MLinrecentyearshavehadtheirgreatestimpactontheinvestmentmanagementindustry.Theindustryhasusedtechnologyfordecadesintrading,clientservices,andback-officeoperations,mostlytomanagelargestreamsoftradingdataandinformationandtoexecutehigh-frequencytrading.However,AI/MLandrelatedtechnologiesarereshapingtheindustrybyintroducingnewmarketparticipants(forexample,productcustomization),improvedclientinterfaces(forexample,chatbots),betteranalyticsanddecision-makingmethods,andcost-reductionthroughautomatedprocesses(Box2).

Comparedtotheinvestmentmanagementindustry,thepenetrationofAI/MLinbankinghasbeenslower.Thebankingindustryhastraditionallybeenattheforefrontoftechnologicaladvancements(forexample,throughtheintroductionofATMs,electroniccardpayments,andonlinebanking).However,confidentialityandtheproprietarynatureofbankingdatahaveslowedAI/MLadoption.Nonetheless,AI/MLpenetrationinthebankingindustryhasacceleratedinrecentyears,inpartonaccountofrisingcompetitionfromfinancialtechnology(fintech)companies(includingfintechlenders),butalsofueledbyAI/ML’scapacitytoimproveclientrelations(forexample,throughchatbotsandAI/ML-poweredmobilebanking),productplacement(forexample,throughbehavioralandpersonalizedinsightsanalytics),back-officesupport,riskmanagement,creditunderwriting(Box3),and,importantly,costsavings.

5

5TheaggregatepotentialcostsavingsforbanksfromAI/MLsystemsisestimatedat$447billionby2023(Digalaki2021).

8IMFDEPARTMENTALPAPERSPoweringtheDigitalEconomy

Box2.ArtificialIntelligenceinInvestmentManagement—SampleUseCases1

?Increasedmarketliquidityprovisionthroughawideruseofhigh-frequencyalgorithmictradingandmoreefficientmarketpriceformation.

?Expandedwealthadvisoryservicesbyprovidingpersonalandtargetedinvestmentadvicetomass-marketcustomersinacost-effectivemanner,includingforlow-incomepopulations.

?Enhancedefficiencywithartificialintelligenceandmachinelearning(AI/ML)takingonagrowingportionofinvestmentmanagementresponsibilities.

?MorecustomizedinvestmentportfoliosbasedonAI/MLtargetedcustomerexperiences.

?DevelopmentofnewreturnprofilesthroughtheuseofAI/MLinsteadofestablishedstrategies.

1SeeWEF(2018)foramoredetaileddiscussion.

Box3.ArtificialIntelligenceinCreditUnderwriting

?Artificialintelligence/machinelearning(AI/ML)predictivemodelscanhelpprocesscreditscoring,enhancinglenders’abilitytocalculatedefaultandprepaymentrisks.ResearchfindsthatMLreducesbanks’lossesondelinquentcustomersbyupto25percent(Khandani,Adlar,andLo2010).Thereisalsoevidencethat,givetheirgreateraccuracyinpredictingdefaults,automatedfinancialunderwritingsystemsbenefitunderservedapplicants,whichresultsinhigherborrowerapprovalrates(Gates,Perry,andZorn2002),asdoesthefacilitationoflow-costautomatedevaluationofsmallborrowers(Bazarbash2019).

?AI/ML-assistedunderwritingprocessesenabletheharnessingofsocial,business,location,andinternetdata,inadditiontotraditionaldatausedincreditdecisions.AI/MLreducesturnaroundtimeandincreasestheefficiencyoflendingdecisions.Evenifaclientdoesnothaveacredithistory,AI/MLcangenerateacreditscorebyanalyzingtheclient’sdigitalfootprint(socialmediaactivity,billspaymenthistory,andsearchengineactivity).AI/MLalsohasthepotentialtobeusedincommerciallendingdecisionsforriskquantificationofcommercialborrowers.1However,financialinstitutionsandsupervisorsshouldbecautiousinusingandassessingAI/MLincreditunderwritingandbuildrobustvalidationandmonitoringprocesses.

1SeeBazarbash(2019)foradiscussionofthepotentialstrengthsandweaknessesofAI/ML-basedcreditassessment.

AI/MLintroducesnewchallengesandpotentialrisks.TheuseofAI/MLininvestmentandbankingdependsontheavailabilityoflargevolumesofgood-quality,timelydata.Withthestorageanduseoflargequantitiesofsensitivedata,dataprivacyandcybersecurityareofparamountimportance.DifficultiesinexplainingtherationaleofAI/ML-basedfinancialdecisionsisincreasinglyanimportantissueasAI/MLalgorithmsmayuncoverunknowncorrelationsindatasetsthatstakeholdersmaystruggletounderstandbecausetheunderlyingcausalityisunknown.Inaddition,thesemodelsmayperformpoorlyintheeventofmajorandsuddenmovementsininputdataresultinginthebreakdownofestablishedcorrelations(forexample,inresponsetoacrisis),potentiallyprovidinginaccuratedecisions,withadverseoutcomesforfinancialinstitutionsortheirclients.

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C.RiskandComplianceManagement

AI/MLadvancesinrecentyearsarechangingthescopeandroleoftechnologyinregulatorycompliance.Regulatorytechnology(regtech)

6

hasassumedgreaterimportanceinresponsetotheregulatorytighteningandrisingcompliancecostsfollowingthe2008globalfinancialcrisis.Forthemostpart,technologyhasbeenusedtodigitizecomplianceandreportingprocesses(Arner,Barberis,andBuckley2017).However,advancesinAI/MLoverthepastfewyearsarereshapingriskandcompliancemanagementbyleveragingbroadsetsofdata,ofteninrealtime,andautomatingcompliancedecisions.Thishasimprovedcompliancequalityandreducedcosts.

MaturingAI/MLtechnologyhasthepotentialto

propelfurtheradoptionofregtechinthefinancial

sector.Accordingtoarecentglobalsurvey,AI/MLis

thetoptechnologyunderconsiderationamong

regtechfirms(Schizasandothers2019;Figure1).

IncreasedadoptionofAI/MLinregtechhas

significantlyexpandeditsusecases,cuttingacross

banking,securities,insurance,andotherfinancial

services,andcoveringawidevarietyofactivities.

Theseincludeidentityverification,anti-money

laundering/combatingthefinancingofterrorism,

frauddetection,riskmanagement,stresstesting,

microprudentialandmacroprudentialreporting,as

wellasco