【国际清算银行】消费者财务数据和非横向合并-2025.3

BISWorkingPapersNo1251

Consumerfinancialdataandnon-horizontalmergers

byLindaJeng,JonFrost,ElisabethNobleandChrisBrummer

MonetaryandEconomicDepartment

March2025

JELclassification:E21,G34,K21,L41,O32

Keywords:antitrust,bigdata,bigtech,competition,data,financialdata,financialservices,mergers,openbanking,opendata,openfinance,payments,personaldata,privacy

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ISSN1020-0959(print)

ISSN1682-7678(online)

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ConsumerFinancialDataandNon-HorizontalMergers

March2025

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Authors:LindaJeng,JonFrost,ElisabethNobleandChrisBrummer1

Abstract

Thisarticleexploresthepotentialcompetitiveimplicationsofnon-horizontalmergerswheretheyinvolveextensiveconsumerdata,includingconsumerfinancialdata.Asdatabecomeincreasinglycentraltofirmstrategy,mergersbetweendata-richfirms,whilepotentiallyleadingtopositiveoutcomes,canalsocreatemarketpowerinwaysnotentirelyaccountedforbytraditionalantitrusttheory.Thearticleconsiderssomeoftheseimplications.Itintroducesnewmetricsforvaluingdatasetsheldbymergingfirmsthatcouldhelpcompetitionauthoritiesevaluatemarketimpactsmoreeffectively.Thearticlethensuggestspotentialtoolstomitigateanti-competitiveeffectsofdata-richmergers.Itadvocatesforfurtherresearchtoadaptcompetitionpolicytodata-centricmergers,allwithaviewtomaintainingopen,innovativeandcompetitivemarketsinthedigitalanddataeconomy.

Keywords:antitrust,competition,bigdata,verticalmergers,non-horizontalmergers,bigtech,datasharing,dataconcentration,dataaggregation,financialservices,dataprivacy,consumerfinancialdata,openbanking,opendata,openfinance,personaldata,economicsofdata

1TheviewsexpressedherearethoseoftheauthorsanddonotnecessarilyreflectthoseoftheBankforInternationalSettlements(BIS),EuropeanBankingAuthority(EBA),oranyotheraffiliatedinstitution.Examplesfromindividualfirmsareusedforillustrationandshouldnotbeconstruedasaformallegalopinionaboutthesespecificcases.TheauthorswishtothankCarolinaAbate,OscarBorgogno,RossBuckley,PabloIbáñezColomo,ScottFarrell,VikramHaksar,DarylLim,PhilippPaech,NoahPhillips,MatteoMannino,LauraVeldkamp,participantsofresearchseminarsattheBISandtheUKFinancialConductAuthority(FCA),andananonymousreviewerfortheirinvaluablefeedback.WethankGeorgeSakkopoulosforresearchassistanceandeditorialsupport,GiulioCornelli,CeciliaFrancoandHaiweiCaofordatasupport,andKarlaPatriciaRamirezSanchezandAlessiaTortatoforeditorialsupport.

Thispaperisadraft.AfinalversionisforthcomingintheFordhamJournalofCorporate&FinancialLaw.

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I.Introduction:EconomicsofDataandMergersinthePaymentsSector

Mergersareamongthemostconsequentialeconomictransactionsinthefinancialmarketplace.Theyallowfirmstodiversifyandspreadriskacrossdifferentrevenuestreamsortostrengthentheirpositionsinspecificmarkets.Theyalsoprovideanalternativeforfirmsseekingtoscaleormovedirectlyintonewindustrysegmentsandmarketswheretheyhavelittlepriorexpertiseorresources.

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Globally,mergerandacquisition(M&A)dealssetarecordin2021with62,590transactions,passing$5trillionUSDintotaldealvalue(breakingthe2007recordof$4.2trillion).2In2022,totaldealvaluefellto$3.63trillion—muchlowerthanthepreviousyear,butstillsurpassingthe2017($3.44trillion)and2020($3.42trillion)totals.3(2023sawafurtherdeclineasglobalM&Adealvolumesfailedtobreakthe$3trillionmarkforthefirsttimeinadecade).4

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Mergersfallintotwomaincategories:(1)horizontaland(2)non-horizontal.5Horizontalmergersinvolvetwocompetingfirmsthatproduceandsellthesameproductsandaregenerallypresumedtoreducecompetition.Non-horizontalmergers,meanwhile,involvefirmsthatoperateatdifferentpointsalongthesupplychainorincomplementarysectors.6

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Becausenon-horizontalmergersdonotdirectlyreducecompetitioninthesamemarket,theyhavetraditionallyreceivedlessscrutiny.7However,thisischangingasaccesstodata

2NiketNishant&NiketNishant,GlobalM&AVolumesHitRecordHighin2021,Breach$5TrillionforFirstTime,REUTERS,Dec.31,2021,

/markets/us/global-ma-volumes-hit-record

-high-2021-breach-5-trillion-first-time-2021-12-31/.

3Id.

4EmilyRouleau,ANALYSIS:DespiteQ4Boost,2023M&ADealVolumesDisappoint,BLOOMBERG,Jan.9,2024,

/bloomberg-law-analysis/analysis-despite-q4-boost-2023-m-a-deal

-volumes-disappoint

5OECD,OECDGLOSSARYOFSTATISTICALTERMS(2008),

/en/publications/oecd

-glossary-of-statistical-terms_9789264055087-en.html.Non-horizontalmergersareacatch-allcategorythatcoversmergerswithelementsofverticalintegration,conglomerateeffects,orboth,andmayalsocontainelementsofhorizontalintegration.Non-horizontalmergersandtheoriesofharmarediscussedinacontextparticularlyrelevantforthispaperintheOECD2023,TheoriesofHarmforDigitalMergers,293(2023),

/daf/competition/theories-of-harm-for-digital-mergers-2023.pdf.Foradefinitionofvertical

andconglomeratemergers

(togetherreferredtoas“non-horizontalmergers”),seeCommissionCommunicationGuidelinesontheassessmentofnon-horizontalmergersundertheCouncilRegulationonthecontrolofconcentrationsbetweenundertakings,2008O.J.C265/6,paras.4–5,

https://eur

-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52008XC1018%2803%29.

6OECDGlossary,supranote5.

7StevenC.Salop,InvigoratingVerticalMergerEnforcement,127YaleL.J.1962(2018),andseegenerally,U.S.DEP’TOFJUSTICE,MergerGuidelines§4.0(1984)(“Althoughnonhorizontalmergersarelesslikelythanhorizontalmergerstocreatecompetitiveproblems,theyarenotinvariablyinnocuous.”).

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andtechnologicalcapabilitiesareshowntoincreasinglyimpactcompetitionacrossthedigitalanddataeconomy.8

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Businessmodelshaveevolvedtoharnessdatatocustomizethemarketinganddistributionofproductsandservicestoconsumers.9Andmanylargemergingfirmscollect(orhaveaccessto)vastamountsofdataontheircustomersaspartoftheirrespectivebusinessmodels.Moreover,manyseektoacquiredata-richfirmssuchasdataaggregators,withaviewtodeepeningorexpandingconsumerdatapoolsacrosssectorsandsegments,leadingtomorenon-horizontalmergers.

Togetasenseastotheriseofdata-drivennon-horizontalmergers,itisinstructivetoconsiderpaymentssectormergertransactionsoverthelastdecade(Graph1).Duringthisperiod,thelargestmergers(bypurchaseprice)havebeenhorizontal(i.e.,dealsinwhichadirectcompetitorisacquiredinthesamemarket)(bluedots).Yetnon-horizontalmergers(reddots)increasedsignificantlyinfrequency,size,aswellasvalueoftheacquirer.Moreover,non-horizontalmergersincludedtheverylargestacquirers(dotsize).Wethusseethatthereisanuptickinnon-horizontalmergersbetweenpaymentandnon-paymentfirms.

8UNITEDNATIONSCONFERENCEONTRADEANDDEVELOPMENT,AssessmentofDominanceorSignificantMarketPower,U.N.Doc.UNCTAD/DITC/CPLP/54(2021),

/system/files/official

-document/ciclpd54_en.pdf

9Indeed,suchisthemarketpowerthatcanbederivedfromdataaccessthat,insomejurisdictions,policymeasureshavebeenpromulgatedtomandate,withtheexpressconsentofconsumers,theflowofcertaintypesofdatafromtheoriginalholdertoapotentialcompetitor.‘Openbanking’initiativesareonesuchexample,tofacilitatetheflowofpaymentaccountsandotherdatafrombankstothirdpartyfirms–typicallyfinancialtechnologyfirms(fintechs).Theaimisoftentoreduceswitchingcostsandenhancecompetition.SeeBASELCOMMITTEEONBANKINGSUPERVISION,SoundPractices:Implicationsoffintechdevelopmentsforbanksandbanksupervisors(BankforInternationalSettlements,Feb.2018),

/bcbs/publ/d431.pdfandPaulAdams

,StefanHunt,ChristopherPalmerandRedisZaliauskas,Testingtheeffectivenessofconsumerfinancialdisclosure:Experimentalevidencefromsavingsaccounts,141J.FIN.ECON.1(2021).

Graph1.MergerTransactionsinthePaymentsSectorHaveProliferated

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PurchasepriceinmillionsofUSdollars,logarithmicscale

Dataupto30May2024.

EachdotrepresentsamergertransactionbyAntFinancial,FidelityNationalInformationServices(FIS),FISERV,GlobalPayments,Mastercard,PayPal,Block(formerlySquare)orVisaascollectedbyPitchBookandRefinitivEikon.Thisexcludesdivestituresandintra-firmoperations.

Mergertransactionsareclassifiedas“non-horizontal”whentheacquiringfirmandthetargetfirmoperateatdifferentstagesalongthesamepaymentchain,asdeterminedbyfirmreports.In“horizontal”mergers,theacquiringandtargetfirmsaredirectcompetitorsinatleastonekeybusinessline.

Theheightofdotsreferstothepurchaseprice,andthesizeofdotstothevalueoftheacquiringfirm.Thesizeofeachdotisproportionaltotheacquiringfirm’smarketcapitalizationonthedayofthedealor,inthecaseofAntFinancial,thevaluationofAntFinancialasofend-2018,multipliedbychangesinthemarketcapitalizationofAlibabaHoldingsrelativetoend-2018.Sources:BIS;PitchBookData,Inc.;RefinitivEikon;authors’elaboration.

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Importantly,non-horizontalmergersinthepaymentssectorarenotinherentlyproblematic.Insomeinstances,theycandriveefficiency,innovation,andevenbetterconsumerservices.10Forexample,insomecircumstancesmergersmightallowfirmstoofferfinancialservicesinwaysthatarebetterintegrated,andwhichofferseamlessuserexperienceandlowercosts.Thisisespeciallythecasewheremergerscombinecomplementarycapabilities,suchaspaymentprocessinganddataanalytics.Meanwhile,inothercases,mergersmightheightenfinancialinclusionbyenablingfirmstoleveragemorediversedatasourcestounderstandandserveclientshailingfromtraditionallyexcludedsegmentsofthepopulation.11

Butnon-horizontalmergers(whetherinthepaymentssectororotherwise)maynotalwayscontributepositivelytosocietyormarkets—andinsomecases,theycancauseanti-

10SeeMergerGuidelines,supranote7,§4.0.

11SeeBASELCOMMITTEEONBANKINGSUPERVISION,supranote9,andKarenCroxson,JonFrost,LeonardoGambacorta&TommasoValletti,Platform-BasedBusinessModelsandFinancialInclusion:PolicyTrade-OffsandApproaches,19J.COMPETITIONL.&ECON75(2023).

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competitiveeffects.Aswediscussinmoredetailbelow,mergersenablehighlytargetedmarketing,pricediscrimination,andpredictiveanalyticsthatrivalswithoutaccesstosimilardatacannotmatch.Consumerdatacangrantauniquecompetitiveadvantagethatcanleadtoanti-competitiveoutcomes,asthemergedentitymightpreventotherfirmsfromaccessingnecessarydata,limitinginnovationandraisingbarriersformarketentry.12

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Regulatorsarealreadyanticipatingthesechallengesbydirectingregulatoryrulemakingtowardsunlockingdataportabilityandpromotingopenbanking.13Inmanyjurisdictions,competitionandfinancialregulatoryauthoritiesviewedtheage-oldbankpracticeofkeepingcustomerdatatothemselvesasahindranceagainstcompetition.14Toincreasecompetitioninthebankingsector,manyjurisdictionsnowrequirebankstosharecustomerdatawithexternalpartieswhenthecustomerhasgivenpermission.However,inthispaper,welookmorebroadlytoconsiderthepotentialcompetitiveeffectsofdataaccessviamergersandofferpotentialmetricstoenhancehowtheseeffectscanbeassessedandmitigated.

Thepaperisorganizedasfollows.PartIIexaminesthewaysinwhichdataaggregationinfluencesmarketpower,highlightinghowaccesstoconsumerdatacanenhance

12SeeOECDGlossary,supranote5.

13SeegenerallyOPENBANKING(LindaJenged.,2022).TheEuropeanUnion(andtheUnitedKingdom,aformerMemberStateoftheEU)haveimplementedtheRevisedPaymentServicesDirective(PSD2)whichaimstoincreasecompetitioninthepaymentssectorandimproveconsumerprotectionby,amongotherthings,establishinganewframeworktofacilitateaccesstopaymentaccountsdata.SeeEU’sDirective2015/2366oftheEuropeanParliamentandoftheCouncilof25November2015onPaymentServicesintheInternalMarket,2015O.J.(L337)35andtheUK’sPaymentServicesRegulations2017,SI2017/752(UK).IntheEU,anewlegislativeproposaltofacilitatethesharingofcertainothertypesoffinancialdata(ProposalforaRegulationoftheEuropeanParliamentandoftheCouncilonaframeworkforFinancialDataAccessandamendingRegulations(EU)No1093/2010,(EU)No1094/2010,(EU)No1095/2010and(EU)2022/2554COM/2023/360final(FIDA))wasannouncedbytheEuropeanCommissioninJune2023.TheUnitedStatesrecentlyjoinedtheranksofjurisdictionsrequiringdata-sharingbybanks.OnOctober22,2024,theConsumerFinancialProtectionBureaufinalizeditsPersonalFinancialDataRightsRule.SeeConsumerFinancialProtectionBureau,FinalRule,RequiredRulemakingonPersonalFinancialDataRights,89Fed.Reg.90838(Nov.18,2024)(tobecodifiedat12C.F.R.pts.1001,1033),andthePressRelease,ConsumerFin.Prot.Bureau,CFPBFinalizesPersonalFinancialDataRightsRuletoBoostCompetition,ProtectPrivacy,andGiveFamiliesMoreChoiceinFinancialServices(Oct.22,2024),

/about

-us/newsroom/cfpb-finalizes-personal-financial-data-rights-rule-to-boost-competition-protect-privacy-and-give-families-more-choice-in-financial-services/.

14Forinstance,in2016theUnitedKingdomCompetitionandMarketsAuthority(CMA)publishedamarketinvestigationreportentitledRetailBankingmarketinvestigation:Finalreport,whichconcluded,amongothers,thatinordertoaddressadverseeffectsoncompetitionwithinthelendingindustryinGreatBritainandNorthernIrelandanintegratedpackageofremediesshouldbeimposedinwhichasetofmeasurestargetedatenhancingsmall-mediumenterprisesaccesstoinformationwouldbeprovided.SeeCOMPETITION&MARKETSAUTHORITY,RetailBankingMarketInvestigation:FinalReport(Aug.9,2016)

.uk/media/57ac9667e5274a0f6c00007a/retail-banking-market

-

investigation-full-final-report.pdfandCMA,TheRetailBankingMarketInvestigationOrder2017(Feb.2,2017),

.uk/media/5893063bed915d06e1000000/retail-banking-market

-investigation-order-2017.pdf.

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competitivepositioningthroughpersonalizedofferings,pricediscriminationandinnovation.PartIIIprovidesexamplesofcurrentapproachestotheassessmentofdata-drivenmergersanddata-basedtheoriesofharmbycitingmergersreviewedbycompetitionauthoritiesintheUnitedStates(US)andEuropeanUnion(EU).PartIVproposesnewmetricstoassistregulatorsbetterassesswhethermergeddatasetscouldleadtoanti-competitivepracticesandconsumerharm,andhighlightsmeasuresofdatacomplementarity,populationcoverage,andconsumeroverlap.PartVthenconcludesbyofferingrecommendationsforfutureresearchandpolicydevelopment.

II.PotentialImpactofDataAccessonCompetition

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Tounderstandhowdata-drivenmergerspotentiallyreshapecompetition,itiscrucialtoexaminetheuniqueeconomicfeaturesofdata.Inthispart,wesummarizethesefeaturesandexploreseveralwaysinwhichdatacanimpactcompetitionconsiderations.Unliketraditionalassets,dataarenon-rival.Inotherwords,aslongasthedataremainvalid,theycanbereusedandrecombinedwithoutlosingvalue.15Thisflexibility,combinedwithadvancementsindataanalyticsandartificialintelligence(AI),enablesfirmstoextractnew,valuableinsightsthatcandrivemarketadvantage.Recognizingtheseeconomicfeaturesofdataexplainswhydataaresuchapowerfulassetandwhytheroleofdatainnon-horizontalmergerswarrantsgreaterscrutiny.16

a.EconomicFeaturesofData

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Dataareacriticaleconomicinput.Theyenablefirmstoderiveinsightsintomarketdynamics,optimizeoperations,andinnovate.Theircentralitystemsfromtheirparticularqualitativefeatures,alongsidethetechnologicaladvancementsthathavemadethemincreasinglyaccessibleandvaluable.17Amongthem,overrecentdecades,theavailabilityofdatahasexplodedastheproductoftechnologicaltrends.First,thecostsofcollectingandstoringdata,facilitatingthedigitizationofeverydayeconomicandsocialactivities

15CharlesI.Jones&ChristopherTonetti,NonrivalryandtheEconomicsofData,110Am.Econ.Rev.2819

(2020).Ofcourse,thenotionthatdataarenon-rivalhadbeendiscussedpreviously.Foroneexample,seeHalVarian,ArtificialIntelligence,Economics,andIndustrialOrganization,inAjayK.Agrawal,JoshuaGans,andAviGoldfarb(eds),TheEconomicsofArtificialIntelligence:AnAgenda,UniversityofChicagoPress(2018).

16Foradiscussionofhowfirmsusedataintoday’seconomiestocreatevalueandcompete,seeDirectorate-GeneralforCompetition,EC,ProtectingCompetitioninaChangingWorld—EvidenceontheEvolutionofCompetitionintheEUDuringthePast25Years:COMP.PA01—Ex-PostEconomicEvaluationofCompetitionPolicy,(Jul.1,2024),

https://op.europa.eu/en/publication-detail/-/publication/c03374f1-3833-

11ef-b441-01aa75ed71a1.

17Foranoverview,seegenerally,JosephE.Stiglitz,InformationandtheChangeintheParadigminEconomics,92AM.ECON.REV.460(2002).SeealsoYanCarriere-Swallow&VikramHaksar,TheEconomicsandImplicationsofData:AnIntegratedPerspective(InternationalMonetaryFundDepartmentalPaperNo.2019/013,Sept.2019),

/en/Publications/Departmental-Papers-Policy

-Papers/Issues/2019/09/20/The-Economics-and-Implications-of-Data-An-Integrated-Perspective-48596.

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havefallensignificantlyastechnologymoregenerallyhasadvanced.18Second,advancesinartificialintelligenceandmachinelearning(AI/ML)aremakingiteasiertoquicklyprocesslargeamountsofdatatoextractgreatervalue.19Thesetechnologicaladvancementshavedrivenmanyofthemostvaluablepublicly-tradedfirmstoincludedatacollectionandprocessingaskeycomponentsoftheirhighlyprofitablebusinessmodels.20

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Theeconomicfeaturesofdatafurtheramplifytheirvalue.Non-rivalrydistinguishesdatafromtraditionalinputslikelabor,capital,ornaturalresources,whichareinherentlyrival.21Moreover,dataareinherentlydecomposableandpossessrecombinantproperties:theycanbecombinedwithotherdatasetstocreateentirelynewdatasetswithdifferenteconomicvalue,offeringunparalleledopportunitiesforinnovation.22

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Thereisextensiveliteratureontheeconomicsofinformationandtheimportanceofinformationaccessincompetition.23Thesharingofinformation(orlackthereof)determineswhichpartieshaveaccesstoinformationandwhichdonot.24Theseinformationasymmetriescanprovidemarketadvantages.Thus,reducinginformationasymmetriescanleadtogreatercompetitionandmarketefficiency.25Moreover,theeconomicvalueofdatacangrow.Thiseconomicvalueisderivedfromtwoprimaryeconomicfunctionsofdata–asinputsintotheproductionofagoodorserviceandasinformationshiftersacrosseconomicagents.26

18MaryamFarboodi,RoxanaMihet,ThomasPhilippon&LauraVeldkamp,BigDataandFirmDynamics(Jan.14,2019)(unpublishedmanuscript),

/sol3/papers.cfm?abstract_id=3334064

.Theyauthorsemphasizethatmarginalcostsofdatacollectionareverylowwheredataaregeneratedasabyproductofeconomicactivity.

19SeegenerallyAjayAgrawal,JoshuaGans&AviGoldfarb,PREDICTIONMACHINES:THESIMPLEECONOMICSOFARTIFICIALINTELLIGENCE(2018).ForaconsiderationofAIinmergers,seeDirectorate-GeneralforCompetition,EC,CompetitioningenerativeAIandvirtualworlds(KlausKowalski,CristinaVolpin&ZsoltZomborieds.,Sept.23,2024),

https://op.europa.eu/en/publication-detail/-

/publication/5530c8ca-7a1f-11ef-bbbe-01aa75ed71a1/language-en.

20IntheirOctober2020quarterlyreportfiledwiththeU.S.SecuritiesandExchangeCommission,Alphabet(theparentfirmforGoogleInc.)reportedadvertisingrevenuesof$37.1billion—generatedbythefirm’sdata-drivenadtargetingservices—makingupabout80%oftotalrevenues.SeePressRelease,Alphabet,Inc.,AlphabetAnnouncesSecondQuarter2020Results(July30,2021),

/Archives/edgar/data/1652044/000165204420000031/googexhibit991q22020.htm

.

21Jones&Tonetti,supranote15.

22Carriere-Swallow&Haksar,supranote17.

23Seegenerally,Stiglitz,supranote17andCarriere-Swallow&Haksar,supranote17.

24Datasharingcanbeparticularlycomplexinthecaseofplatformsthatmatchtwodistinctgroupsofcustomersinso-calledtwo-sidedmarkets.Here,informationfromonesideofthemarket(e.g.,users)maybequiterelevanttotheotherside(e.g.,merchants).SeeJean-CharlesRochet&JeanTirole,Two-SidedMarkets:AProgressReport,37RANDJ.ECON.645(2006).

25JulianeBegenau,MaryamFarboodi&LauraVeldkamp,BigDatainFinanceandtheGrowthofLargeFirms,97J.MONETARYECON.71(2018)andseealsoRochet&Tirole,supranote24.

26Carriere-Swallow&Haksar,supranote17.

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b.PotentialCompetitionEffectsofDataSharing

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Theutilityofdataforanygivenfirmordatauserwillnotbeuniversal.Instead,itwillbecontext-dependentandvarybyindustry,especiallyinthecaseofnon-horizontalmergers.27Basedonageneralsurveyofeconomicliterature,weidentifyfourpotentialeffectsthatdataaccessmayhaveoncompetition.Thislistisnotexhaustivebutillustrateshowdataaccessanddataaggregationcanaffectmarketpowerandcompetition.

i.ImprovedServicesandProducts

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Mergersofdata-richfirmsthatenhancethepoolingofdatacancreateusefulopportunitiestoenhanceefficiencyandsocialwelfare.28Forexample,greateraccesstodataaboutaclientmayallowafirmtooffermoreconvenient,personalizedproductsthatmeettheclient’sneedsmoreeffectively.Moreover,theuseofmergedsetsofcomplementaryconsumerdatamayenableextremelyaccuratepredictionsofconsumerfinancialbehavior.Suchpredictionsmayallowthepost-mergerentitytooffernew,moretailored,andbetterpricedfinancialproductsandservicestoconsumers,suchasinvestmentadvice.Thismaythenfacilitatebetteravailabilityofproductsandservices,andchoiceforconsumers.

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Asafurtherexample,improveddataaccessfromdifferentsourcesmayfacilitatemoreaccurateassessmentsofcreditworthiness,potentiallyenablingthebetterservicingoftraditionallyunderservedmarketsegments.29Thismayresultingreaterdifferentiationofcreditpricing,whichwouldbenefitborrowerswithlowcreditrisk,whilemeaninghigherratesforriskierborrowers.Frostetal(2019)showthatanArgentinebigtechlendercan

27Forinstance,insomemarkets,databrokerscollectinformationfromalternativesourcestocreateoutputsforclients,suchasdigitalconsumerprofilesforadtargeting.Neumannetal(2019)showthat,despitethesophisticationofthemethodsusedthese,canbequiteinaccurateandeconomicallyunattractive.SeeNicoNeumann,CatherineE.Tucker&TimothyWhitfield,Frontiers:HowEffectiveIsThird-PartyConsumerProfiling?EvidencefromFieldStudies,38Mktg.Sci.6(2019).Conversely,forconsumercredit,JagtianiandLemieux(2019)findthattheuseofalternativedatafromnon-traditionalsourceshelpstopredictloandefault.Theuseofsuchdataallowedsomeborrowerstoobtainlowerpricedcredit.Howthecombinationofdifferentdatasetswillworkinpracticethusdependsonspecificitiesoftheindustryanddatasetinquestion.SeeJulapaJagtiani&CatherineLemieux,TheRolesofAlternativeDataandMachineLearninginFinTechLending:EvidencefromtheLendingClubConsumerPlatform,48Fin.Mgmt.1009,1009–29(2019),

/10.1111/fima.12295

.

28Indeed,inviewofthesepotentialbenefits,somejurisdictions,suchastheEUandtheUK,havebroughtforwardspecificpolicymeasurestofacilitatethesharingofpersonaldataattherequestofcustomers(i.e.,openbanking).Thesemeasureshavehelpedtoopenupthefinancialservicessectortonewentrants,includingfintechsandbigtechs.Thesenewentrantsarenowcompetingwithbanksallalongthefinancialservicesvaluechain,notablypayments.SeeBASELCOMMITTEEONBANKINGSUPERVISION,supranote9.

29ZhiguoHe,JingHuang&JidongZhou,OpenBanking:CreditMarketCompetitionWhenBorrowersOwntheData(NBERWorkingPaperNo.w28118,2020),

/abstract=3735686

.

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usedatafromitse-commerceplatformtomoreaccuratelypredictdefaultandserveborrowerswhowereexcludedfrombankcredit.30

ii.MarketDominanceandPriceDiscrimination

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Despitethesepossibleadvantages,mergersofdatainvolveclassicconcernsofconcentrationsofeconomicpower.Aseconomiesbecomeincreasinglydata-driven,thereisgrowingresearchestablishinghowtheaggregationofnewcombinationsofconsumerdata(potentiallyvianewdatasharingpoliciesandpractices)canconveycompetitiv