SSRN-金融科技借贷与债务螺旋-Fintech Lending and Debt Spiral

1UNSWBusinessSchool,UniversityofNewSouthWales,Sydney,Australia,Email:lili.dai@.au2MacquarieBusinessSchool,MacquarieUniversity,Sydney,Australia,email:jianlei.han@.au3MacquarieBusinessSchool,MacquarieUniversity,Sydney,Australia,email:jing.shi@.au;4SchoolofManagementandEconomicsandShenzhenFinanceInstitute,ChineseUniversityofHongKongShenzhen(CUHK-Shenzhen),email:bohuizhang@WeappreciatethecommentsfromseminarparticipantsattheHunanUniversity,HuazhongNormalUniversity,NanjingUniversityofAeronauticsandAstronautics,XianJiaotongUniversity,ZhongnanUniversityofEconomicsandLaw,aswellasconferenceparticipantsatthe2023InternationalForumofFintechandFinancialMarketHigh-qualityDevelopment,the2023FinTech,AI,andDataAnalytics,FAIDAWorkshop,the2023BusinessFinancingandBankingResearchGroupWorkshop,the2023ChinaFinancialMarket,Innovation,ESGAcademicConference,andthe18th2023ChinaFinanceScholarsAssociationMeeting.WethankShanghaiQiaopanTechnologyforprovidinguswiththefintechlendingdataandsupportforourempiricalanalysis.ithalternativedatasources,fintechlendersmaytargetandprotproductstonewcustomers.Wefindtborrowersreceivemorepromotigreatertendenciesofsubsequentnewborrowing,peliteracyandlimitedcreditaccess.Takentogether,consequencesoffintechconsumers’overborrowingbehavior.JELclassification:D14Keywords:Debtspiral;Fintech;Loandelin1“KeycompetitiveadvantagesofFinTechlenders…allowformoreelasticloansupplybutalsohavethepotentialtoinduceoverborrowingbynaïveconsumers.”-Berg,Fuster,andPuri(2022)demandshocks(Fusteretal.2019;2021)andconcernedaboutthebusinessmodelsoffintechlendConsumerFinancialProtectionBureau(CFPB)cautionsthatborrloansbasedonthe“BuyNow,PayLater”modelexhibithighlevelsoffinancialdisoverextensionproblems(CFPB2022;2023).1However,researchontheoverextensionofcreditinthefintechlendingmarketisscarce(Bergetal.consumerscanbetrappedintoadebtspiralthroughthereceiptofmobilemeslenders,aspecificmechanismofenticingnewborrowers.2especiallysignificantforlow-incomeconsumersmayspillovertoconsumers’oconsequencesofconsumers’engagementinoverborrowingbehaviorinthedigitaleratfintechlendingmark1Overborrowing/overextensionforconsumersreferstotwoforms:1)loanstacking,theriskthataborrowertakesoutconcurrentloansatdifferentlendersandisunabletorepaysomeorallofthem,and2)sustainedusage,theriskthatfrequentloanusagemaythreatenborrowers’abilitytomeetotherfinancialobligations,suchasrentandutilities(CFPB2022).2Relatedly,DiMaggioandYao(2021)findsthatfintechborrowersaremorelikelytopurchaseacarafterloanoriginationanddefaultthanthoseborrowingfromtraditionalfinancialinstitutions.Ourstudycomplementstheirfindingsbydocumentingaparticulartoolforfintechlenderstoenticenewcustomersthroughthedisseminationofloanadvertisingmessagestopotentialborrowers.2Withaccesstoalternativecustomers,potentiallyleadingtoborrowers’unsustainableindebtedness.ForiconsumersoftencomplainaboutmobileadvertisementsfromfinlendershavebeenaccusedofmisleadingadveRegulatoryCommission(CBIRC)hashighlightedoverborrowingriskfacedbyfintechboexpenditures,andpersonalinformationleakage(CBIRC2020).thoseborrowerswithalowengageinself-controlproblemsandimpulse-drivenexpenditures,leadingtooverborroacceleratedprocessingtime,andleamplifyborrowers’self-controlprobl2022).WeconsiderthismechanismastheFinancialLiteracyChannel.Second,inthefintechlendingmaccessingcreditservicesaretypicallyassociatedwithahighlevelofpovbytraditionalfinancialinstitutions(Hauetal.canperpetuateitselfbyfurtherunderminingindividuals’self-controlcapacity(e.g.,Beal.2015),thereforecausingoverborroabilitytoexploitaltetreatthismechanismastheCreditThird,theregulatoryoversightandenforcementmechanismsagainst2020).TheconcernsaboutthelackofregulationleadingtofraudwithfiregulatorsinChinacons2023).Forexample,theCBIRChascautionedconsumersagainst“theriskofoverborrowing3Thus,fintechborrowersmaybetrickedintodebtspiralsbecauseofthelackofregulationinthefintechlendingmarkets.WelabelthismechanismastheLenWeinvestigatetheprocedureofhowconsumersmayengageinconsumersthatcanhelpidentifytheirfinancialandsocialconsequences.3Tincluding,forexample,thebalanceofheronlinepaymentaccounts,thehistoricalrecordofpastloanapplications,andthedataofmobilephoncreditworthinessandcomputestheinternalcreditscortheborrowerreceivesmoremobilemessagesfromot2,787,505borrower-dayobservationsfromJuly2017toNovember2019.Theresultsfromthedifference-in-differences(loanapplicationsareapprovedbythefocaldeliberatelytargetborrowerswithapprovedloanapplicationsbecausetheseborrowersmayhaverelativelyhighcreditworthinessandwillhaveastrongcasacquiringborrowers’informationfromalternativedatasourcesandtargetsubjecttorigorouslendingregulationsrestrictingtheuseofaltborrowers.Economically,wefinpromotionalmessagesfortreat3WethankShanghaiQiaopanTechnologyforprovidinguswiththefintechlendingdataandsupportforourempiricalanalysis.4Next,toaddresspotentidesign(RDD)analysesbasedonthefocalfintechlethehigherthescorevalue,thelowerthecreditriskofaloanapapproveanapplicationwhenthestandardizedscorpleswithcreditworthinessscoreswithinasmallrangearoundfvalueofzero.Bydoingthis,weassumeslightlygreaterorlowerthanthevalueofzero(e.g.,creditscoresOurfindingsfromtheRDDanalysesareconsistentwithtWefurtherperformseveraltwo-stageRDDanalysestoinvestigatewhethertreatedborrowersindeedengageinadebtspiralinducedbyotherfintfpromotionalmessagesreceivindicatorvariableoftheborrower’sloanapprovaldecision.Inthesecondstage,weexrelationbetweentheborrower’sactionorconsequenceonthatdayandthepredictedvalueofthepromotionalmessagenumberinstrumentedfromthefirst-stageregression.The[-0.05,0.05].WediscusstheresultsofdebtspiralengaFirst,weshowthattheborrowers’tendencytoregisternewloanswithotherfintechlendifocallender)ispositivelyassociatedwiththepredictedvalueoftheloanpromotionnumber.Weperformtheanalysesinapost-approvaimpactsofloaninitiationwiththefocalfintInfurtheranalyses,wedifferentiatetheloanregistrationsenticedasendpromotionalmessagesandfindtheposinumberholdsforbothtypesofregistrations.Theseresultssuggestthattreatedborrowersarenot4Ourresultsarerobusttoalternativewindowssuchas[61,180]twomonthsor[121,180]fourmonthsaftertheloanapprovaldecision.5onlyenticedtoregisternewloanswithpromotinglendersbutalsoencouragedtoborrowmorefromnon-promotinglendinloanpromotionmessagenumber.Inparticular,forborrowershavingnewloanregistrationswithailylikelihoodofinfexpendituresarepositivelyassociatedwiththepredictednumberofloanpromotionmessagenumberisinsignificantlyrelatedtotheexpendifplatforms,treatedborrowers’personabehaviormaypotentiallyreduceborrowers’futurerepaymentcapabilitithedailylikelihoodofreceivingadebtcollectionmessage(orthedailynumberofdebtcollectionandthepredictednumberofpromotionalmessages.Comparedtoourprevioustestsbasedontheimplythatafterregisteringnewloansandincurringmorepersonalconsumptions,treatedfromlendingplatforms,trappedinFourth,weinvestigatetfocusingonthemobilemessagesthatborrowersreceivewithnegativeChinesephraasignalsuggestingthafadversesocialevents.Wedocumentthatthelikelihoodofreceivingmobilemessageswithnegativephrasesandthenumberofsuchmessagesarepositivelyrelatedtothepredictednumberofpromotionalmessagesinawindow[91,180]threemonthsaftertheloanapprovalbythefocalfintechlender.TheseresultsshowthattreatedborrowersnotonlytendtobutalsomorepossiblyexperienceadverseconsequencesiZinman2014),suggestinganegativesocietalexternalityeffectoftheinducementoffintlendingtodebtoverextension.6willenableborrowerstosatisfytheircashofreceivingfurtherloans,especiallyforthoseconsumerswhohaveshortcredithitomakeloanpaymentsontimfintechlendingpromotionalmessastrongerforborrowerswithalowerleveloffinancialliteracy.Thissuggeststhatborrowerswithlimitedknowledgeandexperiencefortheirpersonalfinancialmanagement,budgeting,andinvestmentaremorevulnerableto(LusardiandScheresberg2013;AChannel.whohavelimitedcreditaccesstotraditionalfinancialseraccessprovidedbyconsulenders,ultimatelyleadingtotheirunsustainableindebtednThird,theresultsfromthecrosinsignificantmoderatingeffectofthelowregulationindicprovince-levelfintechlendingregulationscomessagesandborrowers’overextensionoutcomes.ThesefindingsLendingRegulationChannel,suggestingthatthevariationinfintechlendingregulatialtertheeconomicandsocialconsequencesofconsumersforbeingtrappedindOurpapermakescontributionstotwostpersonalloanscanhelpconsumersmeetfinancialCampbelletal.2012;Gathergo7borrowingdecisionsmaybeinfluencedbycognitivebiasesandlimitations(Bertrandand2011).Wecomplementthifintechlendingplatformswhichenticenewcustomersbyprovidingstreamlinedloanapplicationprocessesthatamplifyborrowers’self-cinformationthroughalternativedatasourlenderstobetterassessthecreditworthinessofcustomersusingalternativepersonaldata2024).Fromtheborrowers’perspective,theyfaceatradeoffbetweentheirpersonaldataprivacyandaccesstocreditfinancing(Chenetal.2021;Tang2021).consumers’personaldatadocumentingthepotentialcostsforfintechborrowerswhenlendersaggrtrappedindebtspiralsandimpulse-drivenconTherestofthepaperisorganizedasfollows.Section2describestheinstitutionalbackground,variableconstruction,andsummarystatisticsthemainresultsandSection4reportstheadditional2.Institutionalbackground,variabledefinitions,anddescriptivestatInthissection,wedescribdefinitionsandsummarystatisticsofthevariablesusedinourmainanaly2.1.InstitutionalbackgroundWecollectthedataonpersonalfintechcompanyinChinathatapplicationsfromprospectiveborrowers,thecompanyestablishedapplicationterminalsinrDuringtheapplicationprocess,applicantsarerequiredtoprovidetheirnationalidentitycainformationandmobilenumbers.Following8utilizestheapplicants’mobilenumbersandnationdatafromthird-partydataproviders,includingtheinformationoftheironlineproviders,thefintechlendercreatestheinternalcreditscorapplicationsshouldbeapprovedorrejected(Bergetal.2020).Afterlenderpromptlycommunicatesthedecisiontoapplicants,normallywithintenminutes.Oncetheloanapplicationisapuseittopurchasegoodsorkeepthecaapplicantsinoursampleopttotakeuptheirloans.Thiscouldpotentstandardizedloantermsandinterestrates,whichareunrelatedtotheirindividualcharacteriSubjecttoloanmaturity,inthenextsixortwelvemonths,theborrowerneedstopaythelenderthemonthlyprincipalandintheborrowertocollectrepayment.Figure1illustratestheprocfloanapplication,approval,repayment,andcollectionbythefintecFurthermore,weobtainthedataofborrowers’personalmobilephonelogsaftertheinitiationoftheloansfromthefocalfintechcompany.dataproviders,whocontinuouslmessagesfromotherfintechlendingplatformsinthepost-loan-initiationperiodanconstructmetricsthatwhicharereflectedinborrowers’subsequentmobilemessagesafterloaninitiations.Toconstructthemeasuresofthereceiptofloanpromotionalmessages,weidentexample,onemessagecanbe:“[AntCreditPay]Congratulations!Youare9inourwhitelistforacreditlineof15,000yuan!Applywithinonehourtogetthemoney.ReplyTtounsubscribe.”ofChina,commercialfirmsarerequiredtoincludetheircompanynameswithinbracketseithethebeginningorendofmobilemessagessenttoconsumers.Thisregulatoryprovisionenablesustoextractthenamesofpotentialfintechlendersfromthesemessages.Subsequently,wemanuallyverifywhethertheextractedtextsindeedpertaintoothernon-focalfintechlenders.Forinsmessagesenttoborrowers.Employingthicompaniesfromthemobilemessagesofborrowers.AppendixApresentsthetoptenreleasingthelargestnumberofmobilemessageswithinoursample.Next,weproceedwithclassifyincategoryofloanpromotionalmessages.Thisclassificationisbasofafullpromotionmessage.Further,werequireacompulsorykeywTechnologyinmessagesformapromotionalmessagesfromthenumberofpersonalloanpromotionmessagesthataborrowerpassivelyreceivesfromothernon-focallendingplatfWealsoidentifyanothercategoryofmoproactivelyapproachlendingplatform,theborrmessagesanddefineMessageProactiveasthedailycountofsuchmessagesreceivedbyaborrowerrelatingtotheproactivebehaviorofsearchingandapproachingotherfintechlenders.InourmainDIDanalyses,weconstructanindicatorvariable,Approval,whichequalsoneifaborrower’sloanapplicationsubmittedtothefoanddefinetheindicatorvariable,Post,whichequalsone(zero)ifaborrower-dayobservationdatedafter(before)thesubmissionoftheborrower’sloanapplicationtFurthermore,weincludeafewcontrolvariablesforaborrower’spersoincludingtheborrower’sage(Age),theindicatorvariableforthefemaleborrowerthecreditscoreprovidedbythefocalfintechlender,withahighva(Score).SeeAppendixCfortheselenderscanleveragethisinformationtotailortheirmarketingeffortstargetiindividuals,whopossessrelativelyfavorablecreditprofilesandaremWefurtherconstructaseriesofmetricstoexaminetheeconomicandsocialconbeingengagedinoverborrowingbehavior.First,weidentibasedontheloanregistrationconfirmationmLoanRegistrationTotalasthedailynumberofpersonalloanregistrationconfirmationmessagesaborrowerreceives.Wefurtherdifferentiatetwotypesofregistrations,i.e.,LoanRegistrationEnticedandLoanRegistrationSpontaneous.Theformerreferstotheenticedloanregisthathavesentpromotionmessagestotheborrowerinthepreviousweekbeforeaparticularday,andthelatterrelatestospontanemessagesinthepreviousweek.5ExpendituresIndicatorandExpendituresAmount,whichareconstructedbasedonthetransaction-relatedmobilemessagessentbyChina.Figure2presentsexamplesofmobilemessByanalyzingthemobilemessagesrelatedtotransactionsfromtheseplatforms,wecandetermine5AspertheregulatoryrequirementssetbytheMinistryofIndustryandInformationTechnologyofChina,lendingplatformsareobligatedtosendconfirmationmessagescontainingverificationcodestoborrowers.Theseconfirmationmessagesallowustoidentifythecorrespondinglendingcompanies.calculatethecorrespondingamountofcashspentbyborrowers(Exvariablesallowustogaininsightsintothedailyconsumptionbehaviorofborrowers,particularlyThird,weconstructtwoloandelinquinvestigatewhetherborrowersexperiencethedelinquencyofloanswithnon-focallendingfirmsbasedonthereceiptofmobilemessagesassociatedwithdebtcollection.Weidentifydebtcollectionmessagesbysearchingfordelinquency-relatedkeywords,including,forexample,“overdue”and“freezeaccount”(seeAppendixB).CollectionIndicatorisanindicatorvariablethatequalsoneifaborrowerreceivesadebtcollectionmessageonaparticulardayfromnon-flendingplatformsandCollectionNumberreferstothedailycountofdebtcollectionmessages.experiencewhenengaginginadebtspiral,ConversationInidentifynegativesocialconsequences,wesearchforspecifickeywordsinborrowers’messages.Thesekeywordsincludetermsinrelationto“divorce,”“breakup,”“suicide,”“detention,”“injail,”“curse,”“bastard,”“fight,”“runaway,”“fraud,”“AppendixB).Byidentifyingtheoccurrenceofthesenegativekeywordsinconversations,wecaninferthattheseborrowersarelikelyengagedintheinitiationofadversesocialevents.WedefineConversationIndicatorasabinaryvariablethatindicateswhonaparticularday,andconstructConversationNumberasthedailynumberoutcomemessages,whichcanhelpusunderstandtheimpactofcreditoverextensiononborrowers’socialwell-being.Lastly,weconstructthreeindicatorvariablesforthecross-sectionalanalyborrowers’characteristics.SpecificaequalsoneifaborrowerisfromaprovSimilarly,wecodeCreditAccessLow(LendingRegulationLow)tobeonewhenaborrowerisfrombasedontheCHFSdata.Byincorporatingthesemetricsintoourcrinvestigatewhethertheefffinancialliteracy,limitedcreditaccess,andweaklendingregulatio2.3.DescriptivestatiOursamplestartswith2,787,505borrower-dayobservationswithapproximmobilemessages,relatingto52,307loanapplicationsbjecttoanalysesfinterestandneeddifferentsampleseleTable1presentsthedescriptivestatisticsofvariablesanaverageborrowerpassivelyreceives0.514loanpromotioreceives0.111messages(MessageProactive)arisingfromherproactivebehaviorovera180-daypost-loan-approvalwindAmongthe2,787,505borrower-dayobabout12.5%ofthemarefemaleborrowerInthesubsamplesfortheconsequencesofeanaverageborrowerreceives0.243messagestriggeredbythenewloanregistratiodebtcollectionmessages(Coadversesocialoutcomes(ConversationIndicator).Ourprimaryanalysesinvestigatewhetherborrowersreceivemoreloanpfintechlender.WeconducttheDIDanalysisbasedoMessage=α+βAP×Approval×+βCV×ControlVariables+βFEFixedEffects+ε,(1)whereMessagerepresentseitherMessagePassive(thedailynumberofpassivelyreceivedloanpromotionmessages),orMessageproactive(thedailycountofmessagestriggeredwhenaborrowerproactivelyapproachesothernon-focalfintechlenders).ApprovalisadummyvariableindicatingPostindicateswhetheraborrower-dayobservationisdatedafterthesubmissioFemale,andScore,aswellastheirinteractionswithPost.Wealsoincludetfixedeffects,FixedEffecandborrower-levelclustering.Table2reportstheresultsofourmainaobservationsandareducedsampleof1,741,417(t-statistic=4.32),suggestingthataborrowertendstoreceivemfromotherlendingplatforms(MessagePassive)afterobtaininglender.Economically,fortpercentinthepost-loan-applicationperrelativetothesamplemean.6WefindsimilarresultsinColumn2fortherestrictedsample.negativeforMessageproactive(bothcoefficients=-0.007arespectively).Theseresultsindicatethatborrowersinthetreatmentgroup,whohaobtainedloansfromftreatmentborrowersha6TheeconomicsignificanceisestimatedasthecoefficientofApproval×Post(0.033)scaledbythesamplemeanofMessagePassive(0.514)=0.033/0.514=6.42%.Takentogether,theresultspresentedinstrategicallytargetborrowerswhohindividualspossessahigherlevel3.2.RegressiondiscontinuitydesiOnepotentialconcernrapprovedbythefocallenthereceiptofloanpromotionmessagessubsequently.Tomitigatethisconcern,weemployasharptheScoremetric,thecreditscoreinternallyconstruThemodelspecification+β6×Score2+βControl×ControlVariablwhereMessagestandseitherMessagePassiveorMessageProactive,andApproval,Post,ControlVariables,andFixedEffectsaredefinedasthesameinEquationinteractiontermsbetweenPost,Approval,Score,andScore2.WhenconductingtheRDDanalyses,werestrictoursampletoborrowaroundthiscutoffvaluresultinginthereducedsRegardingthecontrolvariables,wefindthatthemeanvaluesofAgeandFemaare26.623and0.148forborrowerswithScorewithinaSimilarinferencesholdresultsprovidesupportfortheregressiondiscontinuitydesignanalyseFigure4illustrateshowthechangeintPassive)relatestoScore.Theverticalaxisrepresentsthedifferenceintheaveragenumberopassivepromotionmessages,DayZeroreferstothedatewhenthefocalfintechborrower.ThehorizontalaxispresentsthevalueofScore.higheranddiscontinuedaborrowersobtainingloanapprovalsreceivealargernumberofpromotionmessages.ThispatternprovidesfurthersupportforourRtheinteractionterm,Approval×Post,aresignificantlypositiveColumns(3)and(4),thecoefficientsonApproval×PostarenegativeforMessageProactconcernthatthepositiveeffectofloanapprovalonthereceiptofsubsequentpromotionmessagesfromfintechlendersisdrivenbycertainunobservedpersonalcharacteristicsofborrowers.Weconductadditionalplaceboanalyrandomlyassignnon-zerocutoffvaluestoScoreandthenperformtheredesignanalyseswithinawindow[-0.05,0.05]aroundtherandomlyastimes.Figure5presentsthedistributionofthet-statisticsofregressioncoefficientsonApproval×thusfurtherenhancingtheinferencesdrawnfromourRDDan3.3.Borrowers’financialandsocialoutcomesNext,weinvestigatefourpossiblefinancialandsocialoutcomesoccurringtotheyreceiveloanpromotionmessaloanwithotherlendersandhavegreaterconsumptionwhenmreceived.7Wewilltestthesetwooutcomevarndow[s+91,s+121],i.e.,threemonthsaftertheloanapprovalrexample,thelikelihoodofnewloadays+91whentheborrowerreceivesmorepromotionmessagesinthepreviousweek[s+84,s+90].Wealsoexaminethedelinquencylikelihoodofthenewloaninthewindow[s+121,s+18Forinstance,wepredictthedelinquencylikelihoodtobegreaterondays+12receivesmoreloanpromotionmessageswithin[s+84,s+90],giventhatthefirstmonthlyinstallmentisdueondays+121aftertheregistrationdays+91.Finally,weenewloanregistrationbringsadversesocialoutcomestoaborrowerinthewindow[s+97Ourpreviousanalysessuggestthataborrowerisexpectedtoreceivemorepersonalloanpromotionmobilemessagesfromothernon-focallendersinthewindow[s+1,s+180]thaninawindow[s-180,s-1],assumingtheloanapprovaldecisionismadeondays.8Asmentionedinthepreviousdiscussion,thisempiricalchoiceistomitigatetheconcernregardingthepotentialimpactsofloaninitiationwiththefocalfintechlenderontheoutcomevariables.Theinferencesofourfindingsremainunchangedwhenweusealternativetwo-monthandfour-monthwindows.3.3.1.NewloanregiTable4presentsthetwo-stageanalysesoftherelationbetweenthereceipromotionmobilemessagesandthesubsequentnewloanregistrationsofborrowers.Weemploythetwo-stageRDDmodelspecificationasfollowMessagePassive,Week=α1+β11×Approval+β12×A+βControl×ControlVariables+βFEFixedEffects+ε,(3)LoanRegistration=α2+β21×MessagePassi+β23×Approval×ScoreTheanalysesareperformedat180],forwhichDayZeroreferstothedatewhenthefocalForaborroweronDayt,thvalueofthenumberofperpredictedbytheinstrumentalvariable,Approval,thefocallender’sapprovaldecision.valueofMessagePassive,Week,MessagePassive,Predicted,controllingforborrowers’characteristicsandtheprovinceanddatefixedeffects.Theregistrationmetric,LoanRegistratdailynumberofpersonalloanregistrationconfirmationmessagesthataborrowerreceiveothernon-focallendingplatformsonaparticularday.LoanRegistrationEnticedandLoanfocallenderswhohaveandhavenoweekbeforeaparticularday,respectively.identifytheeffectofpromotionalmessagestriggeredbythefocallender’sapprovaldecisiononalendersignificantlyincreasesthenumberofpassivepromotionmessages.Inthesecondstage,thecoefficientsonMessagePassive,Predictedaresignificthataborrowerwhoreceivesmorepromotionalmessagpromotinglendingplatforms.Wefurtherexaminewhetherthenumb