资本市场中的AI：在创新与市场诚信之间寻求平衡

AIinCapitalMarkets:BalancingInnovationandIntegrity

InshortInthisexploratorystudy,AFMpresentshowAIisreshapingeverystageofthetradinglifecycle:pre-trade,execution,andpost-trade,bringingopportunitiesforinsightandefficiency.Atthesametime,itamplifiesexistingmarketintegrityriskswhilecreatingnew

ones.Recognisingthatfurtherresearchisneededinthisfast-movingdomain,marketfunctioningwillrelyevenmoreonthedesignofAImodels,theirinteractions,andthecontextinwhichtheyoperate.Thefindingsofthestudyaimatopeningandguidingfuturedebate.

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ANALYSIS

APRIL2026

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Executivesummary

Well-functioningcapitalmarketsareessentialtotherealeconomy,supportinghouseholdwellbeingandbroadereconomichealth.

Whentheyareefficientandrobust,theyenablewealthformation,

facilitaterisk-sharing,andallocatecapitaltoitsmostproductiveuse.

AIisincreasinglyintegraltocapitalmarketsandisexpectedtoremainadefiningelementofmodernmarketfunctioning.Usedresponsiblyandgovernedwithclearprinciples,AIcanstrengthenpriceformation,improveefficiency,andsupportmoreinformedinvestmentdecisions.

YetthesamecapabilitiesthatmakeAItransformativealsomakeit

vulnerable.Adaptivemodelslearnquicklyandatscale,stretching

traditionaloversight.TheydependonmodelsIincentiveswhose

integrityisnotalwaysassured,andmayoptimiseinwaysthatare

narrow,opaque,orunpredictable.Evenwhenindividualactorsfollowsoundpractices,theirmodelsinteractwithinatightlycoupledsystem;behaviourscanamplifyoneanother,creatingoutcomesthatnosingleparticipantcanforeseeorcontrol.Familiarrisksreappearinnewformswhileentirelynewonesemerge,oftenfasterandhardertodetect.

Formarketparticipants,AIreshapesnotonlydecisions,butalso

howtheycanbejustified,audited,andgoverned.Forretailinvestors,generalpurposeAItoolsmayappearauthoritativewithoutthe

protectionsofregulatedadvice.Forsupervisors,theshifttoself-

learningmodelschallengesassumptionsaboutexplainabilityand

accountability.Thesepressuresmakeitcriticaltoupholdtrustand

fairmarkets,ensuringthat,throughtherightactions,technological

progressstrengthensratherthanunderminestheconfidenceonwhichmarketintegritydepends.

Achievingthiswillrequirehumanoversight,transparency,and

accountabilitytoevolveaccordinglysothatAIcanrealiseits

potentialwhilesupportingtrusted,efficient,andfaircapitalmarkets.

Threeconclusionsstandout:

1.MarketintegrityisshapedbyhumanchoicesembeddedinAI

systems,especiallyviamodelobjectivesandconstraints,the

contextinwhichtheyoperate,andthedatatheyingest.Asmodelsbecomemoreautonomous,outcomesincreasinglydependonthemodeldesign,thesysteminwhichtheyoperate,thedataquality,andtheresilienceofalloftheabovetomanipulation.Ongoing

humanoversightisessentialtovalidatethatmodelsareethicalandalignedwiththeirintendedpurpose,andtopreventthemfrom

processingorpropagatingdistortedsignals.

2.Risksemergenotonlyfromtheindividualmodelbutalsofrom

theenvironmentinwhichmodelsoperate.Systeminterdependenciescanamplifyimpactassharedinputsandoptimisationtargetsmay

drivecorrelatedbehaviourandfeedbackloops.Supervisionand

regulationmaythereforeneedtoadvancetogethertoensuretimely,effectiveresponsestoemergingsystem-levelrisks.

3.Marketparticipantsremainfullyaccountablefortheoutcomesoftheirsystems,regardlessoftechnologicalcomplexity.Theuseofself-learningmodelsdoesnotdilutethisresponsibility,marketparticipantsremainanswerableforhowtheirmodelsbehave.

Systemsshouldalwaysremaincontrollableandcompliant,withmeasuresinplacetopreventdeceptivebehaviourssuchas

exploitingloopholesinthemodel’sobjectivesorconstraints.

ThefutureofAI-drivencapitalmarketsmayevolveintoamixed

ecosysteminwhichtrusted,well-governedAImodelsinteractwith

lesstrustedandopaqueones,whilecapabilitiesanduse-casesdevelopathighspeed.

ANALYSIS

AIinCapitalMarkets:BalancingInnovationandIntegrity2

AIinCapitalMarkets:BalancingInnovationandIntegrity3

TheAFM’saimistoremainagileandinnovation-aware:promotingtrustworthyAIasthenormwhilemonitoringandmitigating

vulnerabilitiesfromungovernedautonomy,unstableinteractions,andbiaseddata.TrustworthyAIshouldbecomethecompetitive

standard,nottheexception.

Basedonthesefindings,theAFMrecognisesthefollowingprioritiesfordebate:

1.TrustedAImodelsasafoundationofmarketintegrity:the

AFMaimsformarketswhereAImodelsarereliableandsafebydesign.Soundmodelvalidation,clearsafeguardsandstrong

datagovernancewillshapecompetitivedynamics,astrustinAIbehaviourbecomesameaningfuldifferentiator.Accordingly,the

AFMframestransparencyandreliabilityassupervisorypriorities,notjustcomplianceexpectations,astheyconstitutecorefeaturesof

marketfunctioning.

2.SupervisingamixedecosystemoftrustedanduntrustedAIsystems:someparticipantswilloperatehighlygovernedandtransparent

AIsystems,whileothersmayrelyonopaqueorunstablemodels.TheAFMaimstoadaptsupervisionaccordingly:well-governed

AIcanbemetwithmorepredictable,proportionateexpectations,whilehigh-riskoropaquesystemsrequirecloserscrutiny,creatingtherightincentivestructure.Moreover,itisimportanttoreassesswhethertoday’scapitalmarketinfrastructureisstillcalibratedformarketsshapedbyincreasinglyautonomousmodels.AndbeforeAItradingagentsaredeployedatscale,supervisoryauthorities

shoulddefineclearaccountabilitystandardsandregulatepossibleunauthorisedtradingenvironments.Accordingly,theAFMintendstoinitiateanopendialoguewiththesector,whilealigningits

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approachwithEuropeanregulatorsandinternationalstandardsettingorganisations.

3.Addressingsystem-leveldynamicsandpotentialfeedback

loops:marketdynamicsincreasinglydependonhowAImodels

behavebothindividuallyandincombination.Understandingthe

interactionsbetweenthemisessentialtoidentifyingconditionsthatcangenerateself-reinforcingfeedbackloopsandamplifymarket

ANALYSIS

stress.Inthiscontext,abroaderdebateisneeded.Thequestioniswhetheradditionalpossibilitiestoactareappropriateifinteractionsbetweenmodelsdisruptorderlymarketfunctioning.

AIincapitalmarkets:ariskoverview

Thedefinitionofarisk

Crosscuttingandsystem-wide

Poisoneddataasasystemicriskfor

capitalmarkets

AgenticAIexpandstherisksurfacein

high-speedmarkets

Concentration,

correlatedmodels,

andcommondata

sourcesasstructuralmarketvulnerabilities

Readmore

Pre-trade

TreatingGenAIasa

substituteforinvestmentadvice:howinaccuracyandpersonalisation

canharmretailinvestors

AI-washing:misleadinginvestorsthroughfalseorexaggeratedAIclaims

Readmore

Execution

Harmfuloutcomesfromtheuseof

self-learning

algorithmsintrading

Order-flow

exploitationandadeclineinmarketparticipation

Readmore

Post-trade

Opaquemodels:

black-boxbehaviourandpolluted

regulatorydata

Readmore

TheAFMframesriskinaccordancewiththisformat:

“Certaindevelopments,

conditions,andbehavioursofactor(s)thatcanleadtoundesirableoutcomesin

themarkets”.

Theriskdescribed

Quantifiesashighrisk

Likelihoodofhappening

Howtoreadthisriskanalysis?

Potentialimpact

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ANALYSIS

AIinCapitalMarkets:BalancingInnovationandIntegrity4

Contents

Executivesummary2

Introduction6

1.Makingorbreakingmarkets8

2.Threatsspanningthetradelifecycle11

3.Pre-trade16

3.1Pre-tradeopportunities 16

3.2Pre-traderisks 17

4.Execution19

4.1Executionopportunities 19

4.2Executionrisks 20

5.Post-trade23

5.1Post-tradeopportunities 23

5.2Post-traderisks 24

Conclusions26

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ANALYSIS

AFM

AIinCapitalMarkets:BalancingInnovationandIntegrity6

Introduction

Theambitiontocreateautonomoussystemsisnotnew.InGreek

mythology,Talos,thebronzegiantforgedbyHephaestustopatrol

Crete,embodiedboththepromiseandtheperilofartificialautonomy.

Designedtoprotecttheislandwithouthumanintervention,Taloswasimmenselypowerfulyetultimatelyvulnerable:asinglesealedveinrunningthroughhisbronzeframemeantthatonehiddenflawcouldbringthegiantdown.Thisdualnatureechoestoday’sdebatesonintelligent

systems,whereautonomycanenhanceresilienceorundermineit,

dependingonhowwellitsinnerfragilitiesareunderstoodandgoverned.

Inmodernmarkets,thatancientaspirationhasmovedfromallegorytoinfrastructure.AIsystemsnowinfluencehowinformationis

processed,howtradingdecisionsareformed,andhowordersareexecuted,routed,andreconciled.Whatoncebelongedtomyth,

nowunderpinsfunctionscentraltopriceformation,liquidity,andthetransparencyonwhichsupervisoryoversightdepends.

AIcapabilitiesenhanceanalysis,efficiency,andmarketaccess,buttheyalsointroducevulnerabilitiesrelatedtodataintegrity,explainability,modelbehaviour,andinteractioneffectsacrossparticipants.Asadaptivemodelsreplacefixedrules,marketoutcomesincreasinglydependonthedesignandgovernanceofdatapipelinesandalgorithms.

ThisexploratorystudyexamineshowAIisusedacrossthetrading

lifecycle(pre-trade,execution,andpost-trade)andwhatthese

applicationsmeanforthefunctioningandintegrityofcapital

markets.TheAFMfocusesonconcreteareaswhereAIalreadyshapesmarketbehaviour,operationalworkflows,andthemechanismsthroughwhichpricesareformed.

Figure1:Thelinkbetweenthecapitalmarketandtherealeconomy

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Capitalmarkets

Well-functioningcapitalmarketsareessentialtotherealeconomy,supportinghousehold

well-beingandbroadereconomichealth.Whentheyaree代cientandrobust,theyenablecapitalformation,facilitaterisk-sharing,andallocate

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capitaltoitsmostproductiveuse.

R

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Incapitalmarkets,tradingofassetstakeplace,typicallyfollowingthesephases:

123

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C

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Pre-tradeExecutionPost-trade

AIinCapitalMarkets:BalancingInnovationandIntegrity7

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Theaimofthisstudyisthereforetwofold:toclarifywhereAI

createsvalueinmarketfunctioning,andtoidentifywhererisks

mayconcentrateasAIbecomesembeddedintradingsystems.This

supportsmarketparticipantstomakeresponsibledeploymentchoicesandunderstandwhererisksmayariseintheirownoperations,andhowtoaddressthem.Atthesametime,ithelpsregulatorstoanticipate

emergingmarket-widevulnerabilities.Itdoesnotclaimtobe

exhaustiveordefinitive.Thefindingsofthestudyserveasanopeningandguidanceforfuturedebate.

ToframetheouterboundariesofwhatAIcouldmeanforcapital

markets,thereportbeginswithtwoextremescenarios,one

optimisticandonepessimistic,illustratinghowdifferentdesign

andgovernancechoicesmightshapemarketevolution.Itthen

analysescurrentAIapplicationsstepbystepalongthetradinglifecycle,highlightingwherebenefitsemerge,whererisksmaterialise,and

whatthesedevelopmentsimplyformarketintegrity.Examplesandimplicationboxesillustratehowthesedynamicsappearinpractice.

Thisreport’sobjectiveistoexplainhowAIisreshapingopportunities,risks,andintegrityincapitalmarkets.BroaderAIgovernancetopics,

includingprivacy,consumerprotection,cyberresilience,third-party

dependency,andgeopoliticalandstrategic-autonomyconsiderations,falloutsideofthescopeexceptwheretheydirectlyaffectmarket

integrity.TherisksassessedrepresentaweightedsubsetbasedontheAFM’sanalyticalframework.

AsAIbecomesastructuralfactorinmarketfunctioning,integrityconsiderationsmoveevenmoredecisivelytowardsdataquality,modelgovernance,andtheinteractionofautonomoussystems.

Safeguardingmarketintegrityintheyearsaheadwilldepend

increasinglyontheintegrityoftheinformationfeedingthemodels

thattaketradingdecisions.Additionally,justasmythicalautomatons

reflectedtheintentionsoftheircreators,thesemodelsdonotevolveinavacuum:theircapabilities,constraints,andpotentialbiasesreflectthehumanchoicesandintegritybehindtheirdesignandoversight.

ANALYSIS

AIinCapitalMarkets:BalancingInnovationandIntegrity8

1.Makingorbreakingmarkets

TheimpactofAIoncapitalmarketsisneitherpredeterminednor

unidirectional.Thesametechnologiesthatpromisegainsinefficiency,analyticalpower,andfairnesscanalsocarrythepotentialtoamplify

existingvulnerabilitiesandcreatenewrisksformarketintegrity.SinceAIrepresentsmorethananincrementaltechnologicalstep,this

chapterdeliberatelymovesbeyondlinearprojections.Toillustrate

theouterboundariesofhowAIcouldreshapemarketfunctioning,itopenswithathoughtexperimentthatsketchestwohighlycontrastingscenariosofwhatcapitalmarketsmightlookliketenyearsfromnow.Theseutopiananddystopianscenariosformtheouteredgesofa

spectrumalongwhichreal-worlddevelopmentsmayshiftovertime.Theyserveasareferencepointagainstwhichtoday’sopportunitiesandrisksofAIincapitalmarketscanbeassessed.

Figure2:DivergingfuturesofAIincapitalmarkets

Utopianscenario

Scenariox

Scenarioy

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Now

Scenarioz

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Dystopianscenario

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Now2years10years

Morelikelyscenario

Lesslikelyscenario

ANALYSIS

Neitherextremeisarealisticendpointforcapitalmarkets.Themorelikelyoutcomesliesomewheretowardsthemiddleoftheseouter

scenarios,withsomeelementsofbothandtheeventuallandingpointdependingonthechoicesbeingmadetoday.Appropriateregulation,decisionsondatagovernance,modelethics,incentivesanddesign,

andthedegreeofAIautonomymaynudgecapitalmarketsclosertoeitherautopianoradystopiantrajectory.Thequestioniswhether

capitalmarketsaremovingtowardsafutureinwhichAIsupports

ratherthanunderminestheirintegrity,andwhetherhumansremainsufficientlyinthelooptoensurethatoutcome.

Utopianscenario

Inthisscenario,capitalmarketslookclosertotheidealsonce

imaginedinmyth.Theautonomousguardianforgedinbronzeby

HephaestushasbeenrealisedthroughadvancedAIagentsembeddedacrosstheentiretradinglifecycle.Thesesystemsoperatecontinuously,intelligently,andtransparently,shapingmarketswithalevelof

precisiononcereservedfortheory,andtheydosoinwaysalignedwithethicalprinciplesandthebroaderinterestofsociety.

Real-timedataisdigestedanddistributedquicklyandefficientlybyAIagents,increasinglysupportedbyquantumenhancedcomputation.Allparticipants,eitherhumanormachine,seethesamesignalsatthesamemoment.Firmsdiscloseinsideinformationultrarapidlyandpre-tradeintegritychecksensurethatsuchinformationcannotbeexploitedin

trading.Fairnessispreservedwhilemaintainingtheroleofmarkets

inpriceformation,thelatterofwhichbecomesremarkablyaccurate,adjustinginstantlyasnewinformationemerges.Quantumnetworks

alsoacceleratethesynchronisationofmarketdata,allowinginformationtobevalidatedanddistributedacrossparticipantswithaspeedand

fidelitythatcollapsesthelastremnantsofinformationalfriction.

Marketabuse,longachallengeforregulators,evolvesinawaythat

makesitstraditionalformsineffective.Tradingagentsarebuiltwith

embeddedethicalconstraintsandoperatewithinmarketarchitecturesthatmakemanipulationtechnicallychallenging.Theyflaganomalies,coordinatetopreventharmfulpatterns,andrefusetoexecute

manipulativeorders,removingtheincentivesformisconduct.Yetthedisappearanceofabusivestrategiesdoesnotflattenthemarketor

stripitofitsmeaningfuldynamics.Eveninthishighlytransparentand

ethicalenvironment,participantscontinuetotradeduetodifferingexpectations,riskappetites,liquidityneeds,andinvestmenthorizons.

Operationally,humaninterventioninday-to-daytradingrecedesintothebackground.Portfolioconstruction,tradeexecution,monitoringandpost-tradecompliancearenowallmanagedbyinteroperable

AIsystemsthatauditthemselvesandeachotherinrealtime.Inthisenvironment,supervisiondoesnotdisappearbutevolves;regulatorsareconnectedtomarketsthroughrealtime,interoperableoversightsystems,ratherthandetectingabuseafterthefact.Thisreduces

supervisoryburdenandcosts,whileincreasingeffectiveness.

Forinvestorsandfirms,thisenvironmentbringsstabilityand

unprecedentedtrust.Volatilitydrivenbybehaviouralbiasesdiminishes,capitalallocationbecomeslongertermandefficient,andinvestmentflowsgravitatemorereliablytowardsproductiveopportunities.With

informationreflectedinstantlyandaccuratelyinprices,markets

contributemoredirectlytoeconomicgrowthandgeneratesustainedvalueforinvestors.Theyservethepublicinterestbysupportingfair,

well-functioningmarketsthatbenefitsocietyasawhole.Competitionshiftsfromexploitinginefficienciestoenhancingcollective

intelligence.IntodayIsmarketagentsoperatewithanembedded

commitmenttofairconduct.Theirautonomyenhancesthesystem

withouteverbendingitsrules,embodyingaformofintelligencethatishelpfulbydesign.

Thisistheutopianfrontierwheremarketsfunctionaseconomictheoryidealised:fair,transparent,andself-correcting,andwhereAIamplifieshumanvaluesratherthanhumanerrors.

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Dystopianscenario

Inadystopianscenariotenyearsfromnow,capitalmarketsresembletheotherfaceofTalos:highlyautomatedandautonomous,yet

governedbyintricatesystemswhosevulnerabilitiesliehiddendeep

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withintheirdesign.TheimmensepowerandcomplexityembeddedinAI-drivensystemsbecomethethreat:notbecauseautonomycomeswithmaliciousintent,butbecausethearchitecturethatsustainsit

containshiddenpointsoffailure.Eachstepmakessense,eachtrade

appearsrational,andoptimisationislocallyoptimal.Yetcollectively,

theinterplayturnsouttobedisastrous.Thesystemrewardsspeedandstrategicopacityoverdisclosure,solidjudgmentandtrust,privilegingnarrow,one-dimensionalinterestsratherthanthebroaderfunctioningoffairandinclusivemarkets.

Inthissituation,theroleofinformationhasfundamentallychanged.

Informationisnowdesignedandtradedon.Beliefscanbeengineeredandtruthbecomesaparameter.GenerativeAIfloodstheinformationecosystemwithplausible,tailorednarratives.Someinformation

isplainlywrong,whereasotherinformationisoptimisedtosteer

attentioninacertaindirection.Asmodelsdemandevermoredatathantherealworldcansupply,syntheticdatafillsthegap.Misinformation

isdeletedtoevadedetection,butoncelargelanguagemodelshaveabsorbedit,itpersists,reappearinginnewcontexts.Themarketiseasilymanipulatedbytamperingwiththeinputsofthealgorithms

themselves.

Asquantumcomputingbecomesembeddedincapitalmarkets,the

problemdeepens:quantum-enhancedmodelsgenerate,synthesise,andarbitrageinformationatspeedsnohumancanmeaningfullyauditnorreliablydetect.Theseinferenceenginesdonotjustprocessdatabutsimulateitaswell,producinghyper-plausiblesignalsthatblurthedistinctionbetweengenuineinformationandengineeredbelief.Thelastboundarybetweentruthandoptimisationerodes.

Atthesametime,self-learningtradingalgorithmscontinuouslyretrainonalternativedataandlearnabouttheinformationproductionprocess.Subtlenuancessuchasvoicestressinearningscalls,micro-delaysoncorporatewebsiteupdates,andtoneofvoiceduringinvestorQ&As

haveenormouspricefluctuations.Significantshiftsoccurwithout

AIinCapitalMarkets:BalancingInnovationandIntegrity9

headlines,withoutidentifiablemisconduct,andwithoutidentifiableabuse.Marketparticipationbecomesacontestofspeedofinferenceandnarrativecontrol.Equalaccesstoinformationdoesnotsurvive.

Asthisdynamictakeshold,retailandinstitutionalinvestorsrationally

stepbackfrompublicmarkets.Larger,long-horizoninvestorswithdrawandmoveover-the-counter(OTC),whereliquidityconcentratesoutsidethevisiblelandscape.Publicvenuesarehollowedout,leavingbehindaregulatoryghosttownanddestroyingtransparency.Whatremains

onpublicmarketsisbasedonspeedandspeculation,andthepriceismerelyacoordinationsignal.Marketabusenolongerappearsonlyasinsidertradingormanipulationbutalsoemergesendogenouslyasacollectiveoutcomeofmillionsofself-learningalgorithms.

Whatisleftofcapitalmarketsisfree-for-all.AIagentsareallowedtoplan,execute,adapt,andrevisetradingstrategieswithouthumanintervention.Thetradinglifecycle,traditionallydividedintopre-trade,executionandpost-trade,dissolvesaltogether.Agentsmaynotbeexplicitlytrainedtomanipulatemarketsbutsimplylearnthatcertainsequencesofactionssystematicallymovepricesinprofitableways.

Tradingstrategiesemergethatmaximisereturnsbyexploiting

microstructuralfragilities,suchassynchronisedliquiditywithdrawaloramplificationofvolatility.Asignalwarfareemergesbetweenlargetradingfirms.

Supervisionstrugglestokeeppace.Bythetimeariskordeceptivepracticeisunderstoodoridentified,modelshavealreadyevolved.

Complianceandlegislativelogicarenolongerexternallyimposed

becauseAIagentsbegintodraftandenforcetheirowndefactorulesfromwhichtheyoperate.Indoingso,self-learningsystemsminimiselegalexposureratherthanmaximisemarketintegrity.Overtime,theyinferwhichbehaviourstriggersalertsandwhichtradingpaternsare

easiesttojustify.Ineffect,regulatoryreportingissystematicallygamed.Whensuchpracticesbecomewidespread,data-drivensupervision

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losesitsdiagnosticpower.

Ultimately,capitalmarketsspectacularlyfailtoperformtheircore

function:allocatingcapitaltowardstheirmostproductiveuseand

aggregatinginformationintotrustworthyprices.Theconsequences

extendbeyondmarketintegrity.Capitalflowstowardswhat

ANALYSIS

autonomousmodelscanmosteasilyoptimiseandmonetise.Economicgrowthslowsandbecomesmoreuneven,aslong-termwealth

increasinglyaccruestothosewhocontroldata,computingpower,

andaccesstoproprietaryAIsystems.Savingsandinvestmentsare

coordinatedelsewhere,decentralized,withouttransparencyorhumanagency.Inthisenvironment,thefairnessofpublicmarketscollapses

entirely.Marketsnolongeroperateinthepublicinterest,norserveasacommongood.Atthatpoint,thequestionisnolongerhowtofix

AIinCapitalMarkets:BalancingInnovationandIntegrity10

capitalmarkets,butwhethertheystillserveameaningfulpurposeatall.

AIinCapitalMarkets:BalancingInnovationandIntegrity11

2.Threatsspanningthetradelifecycle

ThecontrastingscenariosinthepreviouschapterillustratehowAI

couldreshapethefunctioningofcapitalmarkets.Startingfromthis

chapter,thefocusnarrowsfromlong-termscenariostotheconcretevulnerabilitiesalreadyemergingtoday.Whilemanyarefamiliar,AIcantransmitthemmorequicklyandthroughchannelsthatwerenotpresentinearliergenerationsofautomation.Thiscreatesnewpathwaysthroughwhichlocaldisruptionscanspilloverintobroadermarketfunctioning.

Beforeturningtothreatsthatarisewithinspecificphasesofthe

tradinglifecycle,crosscuttingandsystem-widevulnerabilitiesthat

spanmultiplecyclesareevaluated.AIsystemsandagentsincreasinglyoperateacrosstraditionalboundaries,blurringtheonce-distinct

separationbetweentradingstages.Asmodelscovertheentiretradinglifecycle,riskscanmovemorefreelythroughcapitalmarkets,allowingsmallfrictionstoescalateintoamplificationchainsthatundermine

marketintegrityandresilience.

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Threetradingphases

1

23

Pre-trade

Pre-trade

ExecutionPost-trade

Activitiesinthepre-tradephasetakeplacepriortosendinganorder,includingresearchinto

whatinstrumentstotradewhenandinwhichquantity.

Execution

Here,tradingstrategiesmeettherealworld:

abstractdecisionsaretranslatedintoactual

trades,interactingwithothermarketparticipants.

ANALYSIS

Poisoneddataasasystemicriskforcapitalmarkets

Thesaying“garbagein,garbageout”appliesespeciallywelltoAI

models,includinggenerativeAI.Thesemodelsaretrainedonvast

datasetsandcontinuetolearnfromnew,oftenunlabelleddataonce

deployed.Iftheintegrityofthisinputiscompromised,thebehaviourofthemodelcanbemanipulated,creatingsystemicvulnerabilities.

Attackscanoccuratmultiplestages.Duringtraining,maliciousactorsmaypoisondatasetsoralterlabels,embeddingharmfulbiasesor

misleadingpatterns.Duringdeployment,badactorscantargetthe

modelsIlivedatainputs,introducingmanipulatedsignalsthatdistort

predictionsandskewoutputs.Withoutmanipulatingthepricedirectly,theymanipulatethedatapointsthatareusedbythemodelsandthusmanipulatingtheirbehaviour.Suchdatapoisoningandadversarial

manipulationareamongthemostcriticalthreatstoAIreliabilityand

trustworthiness,astheyunderminethemodel’sabilitytoproduce

accurateandconsistentresults.Theyalsoweakenauditability,becauseanintegrity-compromisedinputlayermakesithard