【国际清算银行】让suptech发挥作用：关于采用的关键驱动因素的证据-2025.11

BISWorkingPapersNo1309

Makingsuptechwork:

evidenceonthekeydriversofadoption

byLeonardoGambacorta,NicoLauridsen,SamirKiuhan-VásquezandJermyPrenio

MonetaryandEconomicDepartment

November2025

JELclassification:G28,O33,C25

Keywords:suptech,financialsupervision,technologyadoption,financialauthorities

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ThispublicationisavailableontheBISwebsite(

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©BankforInternationalSettlements2025.Allrightsreserved.Briefexcerptsmaybereproducedortranslatedprovidedthesourceisstated.

ISSN1020-0959(print)

ISSN1682-7678(online)

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MakingSuptechWork:EvidenceontheKeyDriversofAdoption

LeonardoGambacorta,NicoLauridsen,SamirKiuhan-VásquezandJermyPrenio*

AbstractThispaperexaminestheinstitutionaldriversofadoptingsupervisorytechnology(suptech)byfinancialauthoritiesworldwide.Usingsurveydatafrom

112financialauthoritiesacross97countriesfromtheStateofSupTechReport,weanalysehoworganisationalcharacteristicsandstrategicframeworksshapetheadoptionofsuptechinitiatives.Theanalysisemploysatwo-stagehurdlemodeltotrackadoptionfromproofofconcepttoprototype,andfinallytofulldeployment.

Wefindthatauthoritieswithinstitution-widestrategiesfordigitaltransformation,datagovernance,andsuptechdeploymentuse,onaverage,about20additionalapplicationsandfacefewerdesignandimplementationchallenges.Furthermore,whileanauthority)ssizeandinstitutionalmandatearesignificantfactorsininitiatingadvancedprojects,theestablishmentofadedicatedsuptechunitisthemostcriticalfactorinincreasingthenumberofdeployedapplications.Finally,wefindthatpubliccloudadoptionisassociatedwithahigherprobabilityofimplementingAItools,whilerelianceonin-housedevelopmentisstronglyassociatedwithearly-stageAIexperimentation.

JELCodes:G28,O33,C25

Keywords:suptech,financialsupervision,technologyadoption,financialauthorities

*LeonardoGambacorta(email:

leonardo.gambacorta@

)iswiththeBankforInternationalSettlements(BIS)andaresearchfellowofCEPR.JermyPrenio(email:

jermy.prenio@

)iswiththeBIS.NicoLauridsen(email:

nico.lauridsen@eui.eu

)iswiththeFlorenceSchoolofBankingandFinance(FBF)attheEuropeanUniversityInstitute(EUI).SamirKiuhan-Vásquez(email:

samir.kiuhan@eui.eu

)iswithFBFattheEUIandtheCambridgeSupTechLabattheUniversityofCambridgeJudgeBusinessSchool.WethankSimonediCastriforhelpfulcommentsandsuggestions.WearealsogratefultotheCambridgeSupTechLabandDigitalTransformationSolutions(DTS)forprovidingthedatafortheanalysis.TheviewsexpressedinthispaperarethoseoftheauthorsanddonotnecessarilyreflectthoseoftheBIS,theEUI,ortheCambridgeSupTechLab.

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1Introduction

Financialsupervisionhelpskeepfinancialmarketssafeandstablebypreventingproblemssuchasinformationgaps,moralhazard,andtheabuseoffinancialfirms’marketpositions(Pigou,1932;Akerlof,1970;Stiglitz&Weiss,1981;Jensen&Meckling,1976).Supervisorssetrulesforfinancialinstitutionsandmakesurethoserulesarefollowed,whichwouldbetoocostlyandcomplicatedforconsumerstodoindividually(Llewellyn,1999).Asfinancialmarketsbecomemoredigital,creatingrapidlygrowingvolumesofdataandinformation,newrisksemerge,suchascyberthreatsandclimaterisks.Traditionalsupervisionmethods,designedforsimplertimes,struggletokeeppace.

Thisiswheresupervisorytechnology(suptech)playsacrucialrole.Suptechreferstofinancialsupervisors’useofadvancedtechnologies,suchasartificialintelligence(AI),includingmachinelearning(ML),andcloudcomputing,toimprovehowtheymonitorthemarket,collectdata,anddetectrisks.Aftertheglobalfinancialcrisisandtheriseoffintech,thereisagreaterneedforagile,data-driven,andeffectivesupervision.Suptechhelpssupervisorsidentifyemergingrisks,reduceinformationasymmetries,andstrengthencompliance.However,suptechadoptionhasbeenslowandunevenacrossjurisdictionsandinstitutionaltypesoffinancialauthorities.Manyauthoritieshavestartedsuptechprojects,butfewhavefullyintegratedthesetechnologiesintotheirdailyworkduetobarrierssuchasoutdatedITsystems,alackofclearstrategiesorskilledstaff,andconcernsaboutdatasecurity.

Thispaperaimstobridgesacademicresearchandpolicypracticetoinvestigatethefactorsthatdrivetheadoptionofsuptechbyfinancialsupervisoryauthorities.Toanalysethisphenomenon,weusetheStateofSuptechReport,collectedbytheCambridgeSupTechLabandDigitalTransformationSolutions(DTS)in2024.Thisglobaldatasetcombinesinformationonsuptechadoptionwithorganisationalcharacteristicsandjurisdictionalfactors.Usingahurdlemodel,weexaminehowfeaturessuchasthetypeofauthority,thepresenceofadedicatedsuptechunit,andtheimplementationofstrategicframeworksfordigitaltransformation,datagovernance,andsuptechinfluenceadoptionacrossdifferentstagesofthesuptechlifecycle.WealsoinvestigatetheroleofcloudcomputingasanenablingtechnologyfordevelopingAI-basedsuptechtools.

Ourfindingshighlightthatimplementinginstitution-widestrategiesfordigitaltransfor-mation,datagovernance,orsuptechisstronglyassociatedwithincreasedadoption.Indeed,financialauthoritiesthathaveadoptedallthreestrategiesuse,onaverage,20moresuptechtoolsthanthosethathavenot.Thishighlightsthesignificanceofstrategicframeworksthatactivelyguideandallocateresourcesfortechnologicalinnovationacrossallsupervisoryfunctions.Ourresultssuggestthatestablishingdedicatedsuptechunitsisparticularlyeffectiveintranslatingstrategicintentintotangibleprogress.Theseunitscanfostertechnologicaladoptionbycentralisingexpertise,aligninginnovation

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withinstitutionalobjectives,andovercomingorganisationalinertia.Investingincloudinfrastructureisparticularlycrucial,asitprovidesthescalabilityandcomputationalpowerneededforadvancedanalyticsandAI-driventools,whileenablingauthoritiestomoderniseITsystems.However,cloudadoptionmustbecarefullypursued,payingspecialattentiontodatasecurity.Additionally,authoritiesshouldprioritisecapacitybuildingandskillsdevelopmenttoaddressinternaltalentshortagesandensurethesustainableintegrationofsuptech.Itisalsoimportanttonotethatsuptechdeploymentlagsinemergingsupervisoryareas,suchasclimate/ESG,cyberrisks,anddigitalassets.

Thispapercontributestotheliteraturebyprovidingaclearoverviewofthecurrentstateoftheartinglobalsuptech(Eisenbachetal.,2022;Degryseetal.,2025;Brynjolfsson&Kazinnik,2025).Ithighlightskeyfactorsthatcouldassistfinancialauthoritiesinadvancingthedevelopmentofnewsupervisorytoolsandtransformingtheirorganisationstoenhancesupervisorycapacity.

Theremainderofthepaperisstructuredasfollows.Section2linksourpaperwiththreestrandsoftheliterature:financialsupervision,technologyadoption,andinstitutionalcapacity.Section3outlinesananalyticalframeworkforsuptechadoption,describingthestagesofthesuptechlifecycleandthemaindriversoftechnologicaldevelopment.Section4describesthedatacollectionprocessandthedataset.Section5detailstheconstructionoftheprimaryvariablesandprovidessomedescriptiveanalysis.Section6outlinestheempiricalstrategy,basedonhurdlemodels.Section7presentsthemainresults,andSection8concludesbydiscussingpolicyimplicationsandavenuesforfutureresearch.

2Linkswithexistingpolicyandacademicliterature

Thissectionexamineshowfinancialsupervisionisevolvingthroughtechnologyfromaneconomicandpolicyperspective.Financialsupervisionisessentialforaddressingmarketfailuresthatcreaterisksrelatedtoprudentialconductwithinthefinancialsystem.Theseissuesincludeexternalities,asymmetricinformation,moralhazard,andprincipal-agentproblems(Pigou,1932;Akerlof,1970;Stiglitz&Weiss,1981;Jensen&Meckling,1976).Ifleftunaddressed,thesedistortionscanunderminemarketintegrityandstability.Financialregulationsestablishrulesofbehaviourtomitigatetheserisksanddefineacceptableoutcomesforfirms.Supervisioncomplementsregulationbyensuringthatfirmscomplywiththeserulesandinterveningwhentheiractionsthreatenregulatoryobjectives.Thisoversight,performedbycompetentauthorities,helpsresolvecollectiveactionproblemsandreinforcesthefinancialsystem’sstability.Llewellyn(1999)describesthisasconsumersdelegatingmonitoringresponsibilitiestotheseauthoritiestoreapthebenefitsofexpertiseandeconomiesofscale.Otherwise,consumerswouldfindmonitoringoffinancialfirmscostlyandwouldlikelycreatea‘freerider’problem.

Theacademicliteratureonfinancialsupervisioninthebankingsectorisquitelimited

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duetodataavailabilityandconfidentialityissues.Thisliteratureservesasourreferencepointforstudyingthesuptechphenomenonanditsimpactontheorganisationalstructureoffinancialauthorities.Onestrandaddressesthedeterminantsofsupervisorypractices,focusingontheinternalinformationflowsthatshapemonitoringandreportingactivities,whicharefundamentaltosupervisoryactionssuchasenforcement.Anotherstrandexaminestheeffectsofenforcementactionsonmarketrisk-takingbehaviour.Ultimately,thefocuswillshifttotechnology’simpactonsupervisorypractices.

Supervisors’willandabilitytoactareessentialtoeffectivefinancialsupervision(Adrianetal,2023).Clarityofmandate,strongoperationalindependenceandaccountability,adequateresources,andlegalprotectionforsupervisorsenablethiswillandabilitytoact.ThesefactorsarecapturedinthefirsttwoCorePrinciplesforEffectiveBankingSupervisiondevelopedbytheBaselCommitteeonBankingSupervision(BCBS)andtheInsuranceCorePrinciplesdevelopedbytheInternationalAssociationofInsuranceSupervisors(IAIS).Theseprinciplesserveasminimumstandardsforbankingandinsurancesupervision.Studiesshowthatthesupervisoryarchitectureandgovernance,whichdetermineindependenceandaccountability,influencetheeffectivenessoffinancialsupervision(Masciandaroetal.,2008;Dincer&Eichengreen,2012).

Theliteratureshowstheimpactofenforcementactionsonbanks’risk-taking.Stringentsupervisionthroughenforcementactionanddirecton-siteinspectionsreducesbanks’fragilityandriskexposure(Bassettetal.,2015).Delisetal.(2019)andDelietal.(2011,2016)showthatenforcementactionsagainstbankscanstrengthentheirfinancialstabilitybyreducingrisk-weightedassetsandnonperformingloanratios.Whilethereisnoobservedincreaseinthelevelofregulatorycapital,theresultssuggestthatenforcementactionsimprovebanks’overallhealth.Evensupervisoryscrutiny,actionsthatfallshortofenforcementactions,reducesbankrisk-taking(Degryseetal.,2025).

Financialsupervisorsmusthaveabasisforconductingenforcementactionsorintroducingpreventivemeasures.Supervisors’monitoringactivitiesprovidethatbasis,andtheireffectivenessdependsonhowauthoritiesdeployresources,particularlytheskills,processes,technologies,techniques,andtoolsavailabletothem.Eisenbachetal.(2022)demonstratethattheallocationofsupervisoryresourcesscalesalmostproportionallywithbanksize.Theydiscussthepotentialbenefitsoftechnologicaleconomiesofscaleandscopeinsupervision.Thedeploymentofsupervisoryresourcesshouldenablemonitoringactivitiestoprovidetimelyandreliableinformationaboutfinancialinstitutions’conditionandriskprofile.Traditionally,suchinformationwasobtainedfromregulatoryreports,publicdisclosures,andon-siteexaminations.Forinstance,HirtleandLopez(1999)showthatthefrequencyofon-siteexaminationsshoulddependonhowquicklyprivatesupervisoryinformationerodesinvalue.Now,supervisorsalsousealternativesourcesofinformation,suchasmarketdata,newsarticles,andsocialmedia(Alonso-Robiscoetal.,2025).

Financialauthoritiesareconstantlyimprovingtheirmonitoringactivitiestoenhance

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thequalityandtimelinessofinformationandimprovetheinsightsavailabletothem.Thisinvolvescontinuouslydevelopingsupervisorystaffcapacity(Crisantoetal.,2022),usingnewtechnologiestoenhanceregulatoryreportinganddatacollection(Crisantoetal.,2020),andupgradingITinfrastructureanddataarchitecture(diCastrietal.,2019).Authoritieshavealsoexplored,developed,anddeployednewtechnologiestobenefitfromadvancesindataanalytics(Coelhoetal.,2019;Beermanetal.,2021;GarciaOcampoetal.,2022).Forexample,supervisorsincreasinglyuseMLtoanalyselargevolumesofstructuredandunstructureddata.Inthiscontext,Brynjolfsson&Kazinnik(2025)proposeacomprehensiveframeworktounderstandhowAI,especiallylargelanguagemodels(LLMs),canenhancesupervisoryauthorities’strategicandoperationalcapabilities.AItoolscantransformsupervisoryworkflowsbyimprovingdatacollection,speedingupsignalextraction,andenablingmoreaccurateandtimelyriskassessments.Empiricalanalysisindicatesthatthesetoolscouldenhanceasignificantportionofsupervisorytasks,particularlythoseinvolvinganalyticalanddecision-makingfunctions.However,

thistransformationrequiresinvestmentindatainfrastructure,workforceupskilling,androbustgovernancemechanismstomanagetherisksofintegratingAIintosupervisoryprocesses.

Theterm"suptech",shortforsupervisorytechnology,wascoinedbytheMonetaryAuthorityofSingaporein2017todescribeitsstrategicadoptionoffinancialtechnology.Adualobjectivedrovetheinitiative:toenhancetheeffectivenessofitssupervisoryprocesseswhilesimultaneouslyreducingthecomplianceburdenimposedonregulatedfirms(Menon,2017).BroedersandPrenio(2018)laterformalisedthetermwiththenowwidelyuseddefinitionofsuptech:supervisoryauthorities’useofinnovativetechnologiestosupportsupervision.Theemphasisoninnovativetechnologiesiscrucial,asitpositionssuptechasthesupervisoryequivalentoffintechintheprivatesector.Thisdistinguishesitfromthemore“legacy-based”systemssupervisorshavelongused,andinsteadrefersspecificallytomoderntoolsinvolvingAI/MLandbigdata(diCastrietal.,2019).

Supervisoryauthoritiesworldwideareactivelypursuingsuptechinitiativestoaddresscommonsupervisionchallenges,suchasthegrowingcomplexityofrisksandlimitedsupervisorycapacity(Prenioetal.,2024).Globaladoptionofsuptechisexpandingrapidly,with171financialauthoritiesacross107countriesreportingliveimplementationsby2024,upfromjust54agenciesin2022(Barasaetal.,2025).However,onlyafewauthoritieshavereportedthatsuptechhasbecomeintegraltotheirsupervisoryprocesses(Prenio,2024).Severalfactorsaffectthesuccessofsuptechinitiatives.Strongboardandseniormanagementsupportiskey,asthisshapesresourceallocation.Supportfromtopmanagementalsoenablesthedevelopmentofinstitution-widestrategiesforsuptechadoptionandthegovernancearrangementsneededtoimplementthem.

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3AnanalyticalframeworkforSuptechadoption

Suptechadoptionhasbeenheterogeneousacrosscountriesanddifferenttypesoforganisatio-ns.Inthisbroadinstitutionalcontext,thispaperseekstoanswertheresearchquestion:whatarethekeydriversthatfacilitatetheadoptionofsuptechbyfinancialauthorities?Tostudythefactorsandmechanismsbehindtheadoptionofsuptech,wetakeamicroapproach,lookingatthelifecycleoftechnologicaldevelopment.Thisperspectiveallowsforamoregranularunderstandingofhowinstitutionaldynamics,resourceavailability,andstrategicpolicyframeworksaffectthetrajectoryofsuptechimplementation.

Figure1illustratesfinancialauthorities’lifecycleofsuptechadoption,mappedalongatypicaltechnologydiffusioncurvewithintheorganisation.Itdistinguishesthreelevelsofsuptechactivity:ProofofConcept(PoC),whichexploresthetechnicalfeasibilityofaproposedtoolorapplication;WorkingPrototype(WP),whereafunctionalmodeldemonstratesatool’sdesign,features,anduserinterface;andDeployedSolutions(DP),indicatingfullyimplementedandoperationaltools/applicationswithinsupervisoryprocesses.Thefigurehighlightshowdesignchallengesdominatetheearlystages,whileimplementationchallengesbecomemoresignificantastoolsmovetowardfulldeployment. SuptechadoptiontypicallybeginsatthePoCstage,whereauthoritiesfacefeasibilitychallengessuchastechnologicalconstraints,dataavailability,legalconstraints,andinternalcapacitylimitations.PoCsareprimarilyusedtoidentifypotentialapplicationareas,assesspossibleefficiencygainswithinorganisationalprocesses,andevaluatetheresourcesneededtoscalesolutions.AssolutionsevolvefromWPstoDPs,adoptionacceleratessignificantly.

However,implementationchallenges,suchasdataquality,shortagesofpersonnelwithadequatetechnicalskills,andbudgetconstraints,becomemoreprominentandoftenrepresenttheprimarybarrierstosuptechadoption(Barasaetal.,2024).

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Severalregulatorybodiesaretransitioningfromisolatedpilotprojectstocomprehensivestrategiesthatprovideaunifiedvisionandinstitutionalcoherence.Byembeddingdigitalinitiativeswithinbroadergovernancestructures,financialauthoritiescanenhanceoperationalconsistency,supportworkforcedevelopment,andensuretechnologicalalignmenttoovercomechallengessuchasculturalinertia,siloedoperations,fragmenteddatasystems,andlimitedtechnicalcapacity.Digitaltransformation,datagovernance,andsuptechstrategiesthusrepresentthreedistinctyetinterdependentpillarsofmodernsupervisorypractice.Eachcontributesuniquelytothedevelopmentofeffective,technology-enabledoversight.

1Toanalysethemechanismsunderlyingtheinstitutionalfactorsthatdrivetheadoptionofsuptechbyfinancialauthorities,weconsiderseveraldimensions:i)idiosyncraticcharacteristics(i.e.,sizeandtypeofthefinancialauthority,thecountrywheretheyoperate),ii)theadoptionofstrategicframeworksforenablinginnovation,iii)organisationalmodelsfordesigning,developing,anddeployingapplicationsandiv)thepresenceofcomplementarytechnologiesthathelpscaleadoption.

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Figure1:Suptechlifecycle

AsdescribedinBarasaetal.(2024),thejourneytowardeffectivesuptechadoptionisbuiltuponthreedistinctyetinterdependentstrategicpillars.First,aninstitution-widedigitaltransformationstrategyprovidesaroadmapformodernisingoperations,infrastructure,andworkforcecapabilities,integratingtechnology,data,andhumancapitalunderaunifiedinstitutionalvision.Itoftenservesasanumbrellaframeworkforsuptechanddatagovernancestrategies,fosteringagilityandcoherenceinresponsetoevolvingmarketandtechnologicalconditions.Second,aninstitution-widedatagovernancestrategyestablishesprotocolsfordatamanagement,quality,security,andethicaluseacrossallfunctions,supportingAI-readinessandsuptechdeploymentthroughrobustarchitectureandstewardshipframeworks.Overseenbyseniordataleaders,thedatagovernancestrategypromotesinteroperabilityandsafeguardsprivacyandfairness.Third,asuptechstrategyprovidesstructuredplansforharnessingnewtechnologiestoenhancesupervisoryefficiencyandeffectiveness.Prenio(2024)arguesthatasuccessfulsuptechstrategycoveringexperimentation,development,anddeployment-relatedissuesismorelikelytosucceed.Byoutlininghowtoolswillberolledout,buildinguserskills,andensuringseamlessintegrationwithexistingsupervisorysystems,thesestrategiescanovercomecommonbarrierstoadoption.

Theinteractionamongthesestrategicelementsissynergistic:i)digitaltransformationremovestechnological,structural,andculturalbarrierstoinnovation;ii)datagovernanceensureshigh-integrityinputs;andiii)suptechdeliversmeasurableimprovements,reinforcingthecaseforcontinuedreform.Together,theyformareinforcingcyclethatsupportsresilient,scalable,andfuture-readysupervisoryinnovation.Financialauthoritiesmaypursuedifferentstrategicapproaches:astandalonesuptechstrategywithaclearroadmapforintegratingadvancedtechnologiesintosupervisoryoperations;embeddingsuptechwithinbroaderinstitutionalstrategiesfordatagovernanceordigitaltransformation.A

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datagovernancestrategy,forexample,mayfocusonmanaginginstitutionaldataeffectivelywhileenablingadvancedanalyticsforsupervisorypurposes.Thesestrategiesarenotmutuallyexclusivebutcomplementary,offeringmultipleviablepathwaysforfinancialauthoritiestosupportinnovationandinstitutionaldevelopment.

Totranslatehigh-levelstrategiesintodeployedapplications,financialauthoritiestendtoadoptanoperationalmodelformanaginginnovation.Thismodelreflectsorganisationalconstraintsanddirectlyinfluencesthemanagementofresources,skills,andprocurement.Wefocusonthreeapproaches:Adecentralisedmodelisthemostcommon,whereindividualbusinessunitsdevelopsolutions;thisapproachoffersflexibilityanddomain-specificrelevancebutcanlimitscalabilityandleadtoredundanciesifnotsupportedbypropersuptechstrategies(Beermanetal,2021).Acentralisedhubmodelplacesownershipofallinitiativeswithinasingleunit(e.g.,adedicatedsuptechunitorITunit)toensurestandardisationandalignmentwithinstitutionalgoals.Ahybrid"hub-and-spoke"modelbalancescentraloversightwithdepartmentalexpertisebyallowingeachsupervisionunittoexperimentanddevelopsuptechapplicationsitneeds,supportedbythecentralhub. Finally,weexaminetheroleofcloudcomputingindeployingsuptechtools,particularlyAI-basedones.Cloudservicesoffertheflexibility,scalability,andcomputingpowerrequiredforreal-timeanalytics,forecasting,andautomatedsupervisoryfunctions.Aroundone-thirdoffinancialauthoritiescurrentlyusehybridandpubliccloudservices,indicatingthatadoptionremainscautiousduetoconcernsaboutdataprivacy,cybersecurity,anddatasovereignty(Barasaetal,.2024).ReluctancetochangelegacyITinfrastructures,fragmenteddataarchitectures,andbureaucraticconstraintsalsoimpedetheeffectiveadoptionofAI,eventhoughcloud-basedarchitectures-particularlydatalakesandschema-on-readmodels-offerapromisingpathtowardunified,real-timeaccesstodiversedatasourcesacrossinstitutionalsilos.Despitethesetrade-offs,thegrowingneedforscalableinfrastructureisexpectedtoacceleratecloudadoptionasauthoritiesstrivetobalancesecurity,compliance,andperformancewhilemodernisingsupervisory,policy,andanalyticalfunctions(Araujoetal.,2025;Kazinnik&Brynjolfsson,2025;Prenio,2025).

4Data,variables,anddescriptiveanalysis

Datasources.TheeconometricanalysisinthispaperdrawsonanonymiseddatafromtheCambridgeSupTechLabandDTS,sStateofSupTechSurvey2024

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.Thissurvey,typicallycompletedbyseniorstaffinfinancialauthoritiesworldwide,isacomprehensive,multi-dimensionaltooldesignedtoprovideaglobaloverviewofsuptechadoptionand

2Thedatausedinthisanalysiswereprovidedexclusivelyforresearchpurposesinaggregatedform,inaccordancewiththeconfidentialitytermsestablishedwithStateofSupTechSurveyparticipants.Noindividualorinstitution-levelresponsesweresharedordisclosed,andallanalyseswereconductedinamannerthatpreservestheanonymityandintegrityofparticipatingauthorities.

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implementation.Theanalysisisbasedonresponsesfrom112financialauthoritiesacross97countriesandsixcontinents,ensuringadiverseandgloballyrepresentativesample.Theparticipantsincludecentralbanks,banking,insurance,securities,capitalmarketsregulators,andotherfinancialsupervisorybodies.Aroundtwo-thirdsoftherespondentsarefromemergingmarketsanddevelopingeconomies,ensuringallperspectivesfromregionsoftenunderrepresentedinglobaldiscussionsonfinancialsupervisionmodernisation.The56-questionsurveyincludesamixofmultiple-choice,matrix-style,andopen-endedquestions,manyofwhichareconditionalonearlierresponses.

Thesurveywasconductedintwoparts.Thefirstmandatorysectioncollectsessentialdataonthesuptechlandscape,includingstrategiesorroadmapsfordatagovernance,digitaltransformation,andsuptech.Italsoexploresthenumberandtypesofsuptechapplications,theorganisationalmodelssupportingtheirdevelopment,andthechallengesencounteredduringdesignandimplementation.Thissectionfurtheridentifiesthesupervisoryareaswheresuptechisapplied,suchasAML,consumerprotection,digitalassets,andclimaterisksupervision,alongwithquestionsontheeffectivenessoftools,fundingsources,andtheenablingtechnologiesanddatasciencemethodsusedinsupervisoryprocesses.Theoptionalsecondsectionallowsrespondentstoprovidemoredetailedinsightsintogovernancestructures,datamanagementpractices,collaborativeefforts,andstrategicplans.ItincludesquestionsonthelevelofAIandgenerativeAIadoption,ethicalframeworks,Environmental,Social,andGovernance(ESG)oversight,andchallengesinusingbothstructuredandunstructureddata.Italsocoverstheagency’sinternalcapacity-buildingefforts,availableskillsets,andcollaborationwithpeerauthorities.Thetwo-tierapproachofthesurveyenablesustocapturetheinstitutional