FinancialServicesintheEraofGenerativeAI

FacilitatingResponsibleAdoption

April2025

HongKongInstituteforMonetaryandFinancialResearchHKIMRAppliedResearchReportNo.1/2025

Contents

Pages

Foreword2

Acknowledgements3

ExecutiveSummary4

Chapter1GenerativeArtificialIntelligence:AnOverview6

Evolution,keytechnicalbreakthroughs,andimplicationsforthefinancialservicesindustry

Chapter2

ImplicationsforFinancialServicesIndustryRiskManagementandTalentStrategies

18

Theimportanceofriskmanagementandtalent

Chapter3

InsightsfromMarketParticipantsinHongKong

31

GenA.I.adoptioninthefinancialservicesindustry

Chapter4

TheRoleofRegulation

45

Theinternationalandlocalexperience

Chapter5

ConsiderationsForFosteringResponsibleGenA.I.adoptioninHongKong

55

Thescopeforfurtherfacilitationandcooperation

Conclusions63

AppendixA:BackgroundoftheGenA.I.Survey64

AppendixB:GlossaryofTechnicalTerms66

AppendixC:References70

Foreword

GenerativeArtificialIntelligence(GenA.I.),arapidlyevolvingtechnologyinitsearlystagesofdevelopmentandadoption,representsthelatestevolutioninArtificialIntelligence(AI).GloballyandinHongKong,financialinstitutionsareswiftlyexploringGenA.I.’scapabilities,withmanypromisingusecasesemerging.

Inthefinancialservicesindustry,GenA.I.applicationshavethepotentialtoleadtomorecuratedcustomerexperience,andmoreefficientwaysofprocessingandutilisingdigitalinformation.However,theadoptionofGenA.I.couldalsogiverisetonewrisksandchallenges.ThissuggeststhatamidstacceleratedGenA.I.innovation,itsadoptionrequiresacriticalfocusonsafety,trustandintegrity.InHongKong,authoritieshavebeguntoupdateregulatoryguidelinesandtolaunchinitiativesinsupportofresponsibleGenA.I.adoptionandinnovation.

Motivatedbyrecentdevelopments,thisreportprovidesacomprehensiveoverviewoftheevolutionofGenA.I.anditsbroaderimplicationsforboththefinancialservicesindustryandfinancialregulators.ThereportdrawsonthefindingsfromasurveyandinterviewscommissionedbytheHongKongInstituteforMonetaryandFinancialResearch,whichgatheredtheviewsofmarketparticipantsonthecurrentstateofGenA.I.adoptionamonglocalfinancialinstitutions,theexpectedtrajectoryofGenA.I.developmentinHongKong,andthestrategiesemployedforriskmanagementandtalentdevelopment.

ThereportfindsthattheadoptionofGenA.I.isprogressingsteadilyacrossthefinancialservicesindustryinHongKong.However,therearechallengeshinderingadoption,includingconcernsregardingmodelaccuracy,dataprivacyandsecurity,aswellasconstraintsrelatedtoresourcesandtalent.Theemergenceoflessresource-intensivemodelsandmaturingtechnology,coupledwithregulatoryengagement,islikelytocontributetothebroadeningofGenA.I.adoptionovertime.Basedonthesefindings,thereportoutlinessomeconsiderationsaimedatfacilitatingresponsibleGenA.I.adoptionbythefinancialservicesindustryinHongKong.

WehopethefindingsofthisreportcanhelpinformbestpracticesforaddressingGenA.I.adoptionchallengesinthefinancialservicesindustry,andcontributetodiscussionsonresponsibleinnovationandadoption,aswellasindustry-widecapacitybuilding.Lookingahead,withmaturingGenA.I.technologyandevolvingregulationanticipatedtosupportabroadeningofGenA.I.’sapplicationtoawiderspectrumofactivities,furtherresearchmayalsobewarrantedtounderstandthepotentialforGenA.I.tosupportRegTech,SupTech,andthebroaderpolicysetting.

MrEnochFung

ChiefExecutiveOfficer

HongKongAcademyofFinance

ExecutiveDirector

HongKongInstituteforMonetaryandFinancialResearch

2FinancialServicesintheEraofGenerativeAI

Acknowledgements

WethanktheInsuranceAuthority,MandatoryProvidentFundSchemesAuthority,SecuritiesandFuturesCommission,aswellastheBankingConductDepartment,theBankingPolicyDepartment,theBankingSupervisionDepartmentandtheDigitalFinanceDivisionoftheHongKongMonetaryAuthorityfortheirvaluablecommentsandsuggestions.WearealsogratefulforourcollaborationwithErnst&YoungAdvisoryServicesLimitedindesigningandadministeringasurvey,titledFinancialServicesintheEraofGenerativeArtificialIntelligence:OpportunitiesandRiskManagement,fromOctober2024toJanuary2025,andinconductinginterviewswithvariousmarketparticipants,includingbanks,insurers,andassetandwealthmanagers,aswellasGenA.I.

serviceproviders.Finally,wethanktheHongKongInstituteforMonetaryandFinancialResearchCouncilofAdvisersforAppliedResearchfortheircontinuingsupportandguidancerelatedtotheInstitute’sresearchactivities.

HongKongInstituteforMonetaryandFinancialResearch?April20253

4FinancialServicesintheEraofGenerativeAI

ExecutiveSummary

GenerativeArtificialIntelligence(GenA.I.),arapidlyevolvingtechnologyinitsearlystagesofdevelopmentandadoption,representsthelatestevolutioninArtificialIntelligence(AI).GenA.I.modelscanlearnfromthepatternsandstructuresoftheirtrainingdatatogenerateoutputswithsimilarcharacteristics,beittext,images,audio,orvideo.GenA.I.’skeyattributesofaccessbiilty,versatility,andadaptabiitythusbroadensthepotentialtoautomate,innovate,andenhanceproductivityinthefinancialservicesindustry.

TheadoptionofGenA.I.isprogressingsteadilyacrossthefinancialservicesindustryinHongKong.

75%ofthesurveyedfinancialinstitutionshavealreadyimplementedatleastoneGenA.I.usecase,orarecurrentlypilotinganddesigningusecases,andexploringpotentialinvestmentareas.Thisratioisexpectedtoincreaseto87%withinthenextthreetofiveyears.

GenA.I.adoptionhasbeensomewhathigheramongthelargersurveyedfinancialinstitutions.Amongsurveyedfirms,83%oflargefirmshaverolledoutatleastoneGenA.I.usecaseoraretakingstepstowardsadoption,comparedto63%ofsmallfirms.LargerfirmsweretypicallymoreadvancedintheirGenA.I.adoptionandgeneralpreparedness,whilesmallerfirmswithcomparativelylessresourcesfacedgreateradoptionhurdles.

TheprimaryimplementationsofGenA.I.infinancialservicesremainlargelyinternalandnon-customerfacing.75%ofthesurveyedfinancialinstitutionsviewedGenA.I.asatooltoenhanceproductivityandoperationalefficiency,followedby53%whoviewedGenA.I.asempowermentforinnovationanddecision-making.ThemostcommonGenA.I.usecasesarevirtualassistantsforemployees,withGenA.I.useincomplex,higher-risk,andexternalcustomer-facingapplicationsdependentonfurtherimprovementsintheaccuracyofthetechnology.

Thereareanumberofriskmanagementchallengeshinderingadoption,includingconcernsregardingmodelaccuracy,dataprivacyandsecurity,aswellasconstraintsrelatedtoresourcesandtalent.

WhenadoptingGenA.I.,financialinstitutionsconsideredmodelperformanceandaccuracy(highlightedby95%ofsurveyedfirms),modeltransparencyandexplainability(65%),anddataprivacyandsecurity(64%)asthetopthreerisk-managementconsiderations.

Tostrengthenriskmanagement,financialinstitutionsinHongKonghavemadesolidfirststepstowardsresponsibleGenA.I.adoptionanddevelopment,supportedbyupdatedregulatoryguidelines.ThereisaclearprioritisationoftransparencyandaccountabilityinGenA.I.tools,alongsideastrongemphasisondataprotectionandsafeguardingcustomerinformation.Ongoingdatamonitoringandgovernanceofmodeloutputs,andenhancingdataqualitycontrolsandchecks,werecommonpriorityareasidentifiedforimprovement.GenA.I.-relatedcybersecurityawarenesstrainingandregularsecurityassessmentstoidentifyvulnerabilitiesarealsobeingintroduced.

A‘human-in-the-loop’approachisalsoconsiderednecessarytoensureproperriskmanagementandcontrols,especiallyatthisstageofGenA.I.technologymaturity.Consistentwiththisapproach,about80%ofthesurveyedfinancialinstitutionsidentifiedtechnicalskillsrequiredintheuseanddevelopmentofGenA.I.assomeofthetopskillsgapsfacedbytheindustry.60%ofsurveyrespondentsalsohighlightedcomplianceskillsasakeyskillsgapinsupportingGenA.I.initiatives.

Reskillingandupskillingappearstobeacorepartoffinancialinstitutions’talentstrategy.Tohelpbridgeskillsgaps,thesurveyedfinancialinstitutionsareoptingforacombinationof

HongKongInstituteforMonetaryandFinancialResearch?April20255

ExecutiveSummary

upskillingexistingemployees,hiringnewtalent,andestablishingexternalpartnerships,especiallywithGenA.I.serviceproviders.Thereisalsoanemphasisoncontinuouslearningandinnovationandonupskillingexistingdataandtechnologyteamsinareasofdatascience,cloudcomputingandbusinessanalytics.

Theemergenceoflessresource-intensivemodelsandmaturingtechnology,coupledwithregulatoryengagement,arelikelytocontributetothebroadeningofGenA.I.adoptionovertime.Therecentemergenceoflessresource-intensivemodels,suchasDeepSeek-R1,ischallengingtheprevalentviewthatscalingGenA.I.requiresvastcomputingpowerandinvestment.Newapproachestolanguagemodellingarealsoimprovingmodelaccuracyandgeneralperformance.ThetrajectoryofthesedevelopmentsshouldsupportabroadeningofGenA.I.useovertime.

InrecognitionofthetransformativepotentialofGenA.I.,andtofacilitateresponsibleadoption,theregulatoryandpolicylandscapeofAIregulationhasbeenanevolvingprocessacrossjurisdictionsworldwide.Regulatorsgloballyhavebegunre-examiningtheirregulatoryframeworksgoverningAIdevelopmentandadoption.MultilateraldiscussionsamongglobalpolicymakersarealsounderwaytounderstandthepotentialimplicationsofGenA.I.adoptionforfinancialstabilityandmarketintegrity.

Althoughjurisdiction-specificregulatoryframeworksarecurrentlydiverseintermsofthedegreeofcodification,industrycoverage,andsandboxingandfacilitation,thesecontinuetobeguidedbyanendgoaloffacilitatingresponsibleadoption,andbalancingbetweeninnovationandsafety.Achievinggreatercross-jurisdictionharmonisationovertimecanhelpreducethecostsofcompliance

forfinancialinstitutions,especiallythosewithasubstantialglobalfootprint,aswellashelppreventregulatoryarbitrage.

Basedonthesefindings,thereportoutlinessomeconsiderationsforfacilitatingresponsibleGenA.I.adoptionbythefinancialservicesindustryinHongKong.TheHongKonggovernmentandfinancialauthoritieshavebeenactiveinundertakingamulti-prongedapproachinsupportingresponsibleGenA.I.adoptionanddevelopment,withthereleaseofpolicystatements,regulatorycircularsandguidelinessurroundingtheuseofGenA.I.,andthelaunchoffacilitationmeasuressuchastheHongKongMonetaryAuthority(HKMA)andCyberportGenA.I.SandboxforbanksandtheCyberportAISupercomputingCentre.

Inthenear-term,continuedadjustmentsoffinancialinstitutions’riskmanagementframeworkstoalignwithbestpracticescanfosterfurtheradoption.Expandingthescopeoffacilitationmeasures,aswellassupportingtechnologyandimplementationinfrastructure,canalsofacilitateresponsibleGenA.I.adoptionandspurinnovationanddevelopment.

Inthemedium-term,surveyandinterviewssuggestgreatercollaborationamongregulators,industryplayersanddevelopers,cross-jurisdictionregulatoryengagement,andsustainedlong-terminvestmentindigitalinfrastructurearecrucialasGenA.I.adoptionbroadens.

Thesurveyedfinancialinstitutionsviewedthedevelopmentofmoreadvancedusecasesandapplicationsasthetopareawhereindustry-regulator-developercooperativegainsaremostlikelytooccur,followedbytalentdevelopment,enhancedpublicunderstanding,andinfrastructure.

Chapter1

GenerativeArtificialIntelligence:AnOverview

HIGHLIGHTS:

?GenA.I.’skeyattributesofaccessibility,versatility,andadaptabilitybroadensthepotentialtoautomate,innovate,andenhanceproductivityinthefinancialservicesindustry.Byautomatinglabour-intensivetasksandaugmentingworkersincognitivetasks,GenA.I.canalsofreeuphumancapitalforstrategicplanning.

?However,similartoothertechnologicaladvancementsintheearlystagesofdevelopmentandadoption,GenA.I.alsointroducespotentialrisksthatposechallengesforfinancialinstitutionsasprospectiveend-users,andforfinancialauthoritiesthroughtheimplicationsforfinancialstability,andconsumerandinvestorprotection.

?ResponsibleGenA.I.adoptionthusrequiresfinancialinstitutionstoconsidertherobustnessoftheirriskmanagementandtalentstrategies.Financialregulationisalsoanticipatedtobeadynamicprocess,owingtothenascentnatureofthetechnology,andthespeedandbreadthoftheintegrationofGenA.I.intothefinancialservicesindustry.

HongKongInstituteforMonetaryandFinancialResearch?April20257

Chapter1:GenerativeArtificialIntelligence:AnOverview

GenerativeArtificialIntelligence(GenA.I.),arapidlyevolvingtechnologyinitsearlystagesofdevelopmentandadoption,representsthelatestevolutioninArtificialIntelligence(AI).Itsdevelopmenthasbeenunderpinnedbyadvancementsincomputingpowerandefficiency,breakthroughsindeepmachinelearningarchitecture,andtheavailabilityoflargedatasetsthathavefacilitatedthetrainingofcomplexfoundationmodels.GenA.I.modelscanlearnfromthepatternsandstructuresoftheirtrainingdatato‘generate’outputswithsimilarcharacteristics,beittext,images,audio,orvideo.GloballyandinHongKong,financialinstitutionsareswiftlyexploringGenA.I.’scapabilities,withmanypromisingusecasesemerging1.ThischapterprovidesanoverviewofGenA.I.,includingthekeymilestonesandtechnicalbreakthroughs,itskeyattributesandrisksfeatures,anditsbroaderimplicationsforboththefinancialservicesindustryandfinancialauthorities.

Chapter1

1.1ABRIEFHISTORYOFAI

ThebirthofAI

AIisintelligenceexhibitedbymachinesandtheabilityofmachinestoperformtaskscommonlyassociatedwithhumanintelligence.Theinventionoftheprogrammableelectroniccomputeratthebeginningofthe20thcenturyfirstpromptedseriousconsiderationofAIanditspossibilities.Sincethen,AIresearchhasprogressedthroughanumberofdistinctdevelopmentalparadigms2,inareflectionofbothshiftsinAIideologyandthegradualadvancementofthetechnology(Figure1.1).

Figure1.1:MajorstagesinAIdevelopment

ANNsand

DeepLearning(2000s-2010s)

MachineLearning (1990s)

GenA.I.

(2020s)

Rule-based

AISystems

(1950s-1980s)

Source:HKIMRstaffcompilation.

InitialAIresearchwasfocusedonhowtoencodelogicreasoningintomachines,andthedesignofAIsystemsthatfollowspecificrulesandperformwell-definedtasks3.Oneofthefirstnaturallanguageprocessing(NLP)chatbots,ELIZA,wasdevelopedduringthisperiod.Althoughthisperiod

culminatedinthearrivalofexpertsystemsthatemulatetheknowledgeandreasoningabilitiesofhumanexpertsinspecificdomainsrequiringnarrowbutdeepknowledge,thedifficultyofsuchrule-basedAIsystemsinhandlingambiguousandnoisydata4ultimatelylimitedanyreal-worldapplicationsandadoption.

1Mckinsey&Company(2023).

2EuropeanCommission(2020).

3TwoattendeesoftheDartmouthSummerResearchProjectonArtificialIntelligence,HerbertSimonandAllenNewell,proposedmorespecificallythathumanmindsandmoderndigitalcomputerswere‘speciesofthesamegenus,’namelysymbolicinformationprocessingsystems–bothtakesymbolicinformationasinput,manipulateitaccordingtoasetofformalrules,andinsodoingcansolveproblems,formulatejudgements,andmakedecisions.

4Thetranslationofhumanknowledgeintologicalrulestosolveevermorecomplexreal-worldproblemsprovedcomputationallyexpensiveandimpractical,contributingto‘knowledgebottlenecks’.

Chapter1:GenerativeArtificialIntelligence:AnOverview

Machinelearning

Fromthe1990sonwards,steadyadvancesincomputationalpowertotrainandrunAImodelsandprogressinmachinelearning(ML)algorithmsthatleveragestatisticaltechniquestoprocessinformationbegantosupportbetterpredictionsanddecision-makingbasedonhistoricaldata.Theuseofbackpropagation,aniterativealgorithmthathelpstominimisethecostfunctionbydeterminingwhichweightsandbiasesshouldbeadjustedbymovingdownthegradientoftheerror,alsoexperiencedaresurgenceoverthisperiod.Byenablingmoreefficienttraining,backpropagationhelpedtoinspiretherenaissanceandinterestinartificialneuralnetworksinAIresearch.Asaresult,MLdefinitivelyshiftedfromarule-basedknowledge-drivenapproachtoadata-drivenapproach.

Artificialneuralnetworks(ANNs)

Artificialneuralnetworks5,withtheirtopologyofinterconnectedfunctions(‘neuralnodes’),aimtosimulatethewayneuronsinthehumanbraindecipherinformationandsignaltooneanother,andaretrainedtorecognisepatternsand

inferrulesthroughprocessinglargeamountsofinputdataacrosssuccessiveand‘hidden’neuralnetworklayers(Figure1.2).TheintegrationofMLalgorithmsintothistypeofAImodelarchitectureenabledthemodeltolearnmathematicallycomplexrelationshipsbetweendatapointsthroughnumerousiterations,andhasgreatlyexpandedthepotentialforAIinreal-worldapplicationsandadoption.

Ineachlayer,theweightsoftheneuralnodesareadjustedthroughtheapplicationofstatisticalmethodstoprioritisethoseinputdatafromprecedingnodesthatcontributetoimprovingthedesiredoutput,untilthebestfittingmodelthatminimisesthecumulativeerrorfromallthetrainingdatapointsisfound.Oncetrained,neuralnetworksarefurthercalibratedandvalidatedtominimiseerroronapreviously‘unseen’segmentoftheinputdataset(the‘testdata’),toimprovetheirrobustnesstonoise.Thetrainedandvalidatedneuralnetworkmodelcanthenbeusedtointerpretnewinputsandmakedecisionsorpredictions.Themorelayersandinterconnectedneuronsperlayeraneuralnetworkhas,thelargerandmorediversethetrainingdataandcomputingpoweritrequires.

Chapter1

Figure1.2:Anexampleschematicoftheworkingofartificialneuralnetworks

Input1

Input2

Input3

InputN

InputlayerHiddenlayersOutputlayer

Output

Source:HKIMRstaffcompilation.

5Artificialneuralnetworkswerefirstproposedin1943byWarrenMcCulloughandWalterPittsfromtheUniversityofChicago,withthepublicationofthefirstmathematicalmodellingofaneuralnetworkandhowitmightperformsimplelogicalfunctions.Thefirsttrainableneuralnetwork,thePerceptron,wasdemonstratedbyCornellUniversitypsychologistFrankRosenblattin1957.

8FinancialServicesintheEraofGenerativeAI

Chapter1:GenerativeArtificialIntelligence:AnOverview

HongKongInstituteforMonetaryandFinancialResearch?April20259

Deeplearning

Theabilityofneuralnetworkstotakeonadditionallayershasimprovedovertimethroughnewtrainingtechniques6.However,itwastheintegrationofhigh-performancegraphicsprocessingunits(GPUs)andthusmoreefficientparallelprocessing7,aswellasadditionalaccessto‘bigdata’setsthroughcloudservices,thatreallyfacilitatedthetrainingandrunningofmulti-layered(‘deep’)neuralnetworks.Greatercomputationalpoweralsosupportedmoresophisticatedlearningapproaches,expandingthetypeofdatausedtotrainthealgorithms,whilereducingthedegree

Chapter1

ofhumaninterventionandincreasingin-modelfeedback(Figure1.3).Inrecentyears,deeplearninghascometounderpintheAIsystemsbehindnearlyallhigh-performingpredictiveandprescriptiveAIapplications,fromforecastingandfrauddetectiontooptimisationandlogistics.Today,large-scaleAIsystemscanhavehundredsoreventhousandsofhiddenneuralnetworklayersthatmimicthecomplexdecision-makingpowersofthehumanbrain,andexcelatthetypeofreal-worldtasksthatearlierrule-basedAIsystemsstruggledwith.

Figure1.3:Comparisonofdeeplearningapproaches

Inputdata

(states&actions)

Inputdata

(unlabelled)

Inputdata

(labelled)

UnsupervisedLearning

SupervisedLearning

ReinforcementLearning

Error

Error

Reinforcementsignal

Critic

Output

OutputCritic

Output

(mapping)(classes)(state/action)

Sources:IBMandHKIMRstaffcompilation.

TheeraofGenA.I.

EvolutioninGenA.I.hasmirroredtheevolutioninbroaderAIresearch.TherewasearlyGenA.I.experimentationwithstatisticalmodelsforspeechrecognitioninthe1950sandsomesuccessinlanguagemodellingtasksbythelate1980s8.However,GenA.I.applicationsforalongtimestruggledtomovebeyondsimpledetectiontothe

efficientgenerationofhigh-qualityanddiversenewcontent.ThisreflectstheevenhigherdemandofGenA.I.thantraditionalAIforlargeanddiversetrainingdatasetsthatcanadequatelycapturetheintricaciesandvariationspresentintherealworld9,andforcomputationalpowerthatcantrainandoperatethecomplexintegratedalgorithmsoflarge-scalefoundationmodels10.

6Theinventionofthe‘greedylayer-wisepre-training’techniquein2006allowedeachlayerofaneuralnetworktobetrainedindividually,therebyreducingtheaggregateamountoftrainingtime.

7GPUs,originallydesignedforuseincomputergames,packthousandsofprocessingcoresontoasinglechipandtheirarchitectureissimilartothatofneuralnetworks.High-performanceGPUshaveaparallelarchitecturethatallowparallelprocessing,thatis,runningtwoormorecentralprocessingunits(CPUs)tohandleseparatepartsofanoveralltask,reducingtheamountoftimetorunaprogramme.

8Theintroductionofrecurrentneuralnetworksinthelate1980sandlongshort-termmemorynetworksin1997enhancedtheabilityofAIsystemstoprocesssequentialdata.Betweenthe1980sand2000s,theshifttowardsstatisticalmodelsandMLbegintoleadtomorepracticalNLPapplicationsliketranslationservices,searchengines,andvoice-activatedassistants.

9Alzubaidi,L.etal.(2023).

10Hu,Q.etal.(2024).

Chapter1:GenerativeArtificialIntelligence:AnOverview

Chapter1

Theacceleratedprogressionofdeeplearningandprobabilisticmodellingtechniquesintheearly2010sthusalsoledtoajumpintheproductivityofGenA.I.models,throughfacilitatingaseriesofbreakthroughsinGenA.I.modelarchitecture

(Figure1.4).Newtransferlearningtechniques11andthearrivalofmultimodalmodels(e.g.text-to-image,text-to-music)furtherhelpedtodriveefficiencyanddiversityinnewcontentgeneration.

Figure1.4:KeybreakthroughsinGenA.I.architecture:usecasesandmodels

FOUNDATIONMODELS

AlphaGo,DALL-E,NVIDIA

StyleGAN

AmazonAlexa,GooglePhotos,

Spotify

DALL-E2,StableDiffusion,Midjourney,ERNIE-ViLG

BERT,GoogleTranslate,

OpenAI’sGPTmodels,

DeepSeek,ERNIE

Realistic

imagesand

videos

Imagesandtext

Realistic

imagesand

videos

Predictivetext

a