反金融犯罪中的治理代理人工智能模型

GoverningAFCAgents

HowmodelgovernanceandvalidationmustevolveforagenticAIinfinancialcrimecompliance

Abstract

FinancialinstitutionsarebeginningtodeployagenticAIinanti-financialcrime(AFC)systemswhiletheregulatorylandscapecontinuestoevolve.Existingsupervisoryguidanceonmodelriskmanagement(MRM),emerginghorizontalAIgovernanceframeworkssuchastheEUAIAct,andindustryprinciplesforAIinfinancialcrimeprovideimportantgovernanceprinciples.

YetnoneprovidesacompleteframeworkforagenticAFCsystems,whoseautonomous

planning,tooluse,dynamicworkflows,anddelegateddecision-makingintroduceadistinctivegovernanceproblem.

ThispaperproposesapracticalgovernanceframeworkforagenticAFCsystemsthatinstitutionscanapplytoday.Ratherthanreplacingexistingguidance—includinginteragencymodelrisk

managementguidance,theWolfsbergPrinciplesonAI,andtheFinancialActionTaskForce’s(FATF)workonAI—itidentifiesthegovernanceprinciplesthatremainapplicableacross

existingsupervisoryguidanceandintroducesadditionalcontrolsneededtogovernagenticsystemsoperatinginAFCenvironments.

Throughoutthepaper,weusetheUSOfficeoftheComptrolleroftheCurrency(OCC)interagencymodelriskmanagementguidanceasaworkedexamplebecauseitprovidesagovernance

lifecyclefamiliartomanycomplianceandmodelriskteams,illustratinghowexistinggovernanceprinciplescanbeextendedtoagenticAFCsystems.TheresultingrecommendationsareintendedtostandasagovernanceframeworkforagenticAFCsystemsintheirownrightandcanbe

appliedacrosssupervisoryandregulatoryregimes.

Whetherdevelopedinternallyorsourcedfromathirdparty,eachmaterialagentshouldconnecttoanenterpriseagentassurancelayerthatallowstheinstitutiontoconfigure,test,observe,

challenge,constrain,andstopthesystem,andtoretaintheevidenceneededforoversight.

Thecentralargumentisthateffectiveoversightmustshiftfromgoverningadiscretemodeltogoverningthebroaderagenticsystem.Theinherentmaterialityofthatsystemisdeterminedbythebusinessusecase,butitsresidualrisk,andthecorrespondinggovernanceandcontrolsitrequires,canbeshapedthroughengineeringchoicesaroundautonomy,toolpermissions,workflowconstraintsandruntimesafeguards.InstitutionsthatadoptthisbroadergovernanceperspectivewillbebetterpositionedtodeployagenticAIsafely,effectively,andinlinewith

evolvingsupervisoryexpectations.

©OliverWyman2

Contents

Abstract

2

1.GoverningagenticAIforAFCbeforededicatedguidanceexists

4

2.DefiningtheagenticAFCsystem

7

3.ApplyingexistinggovernanceprinciplestoagenticAFC

9

4.Agenticriskandarisk-basedmaterialityframework

11

5.Agentdevelopmentandsystemuse

16

6.Agentvalidationandmonitoring

19

7.Governance,controls,andauthority

26

8.Vendorandthird-partyagenticsolutions:

Assurancewithouttransferofaccountability

28

9.Workedexample:

AnAMLinvestigationagent

31

10.Fromframeworktoinstitutionalpractice

35

11.Conclusion36

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1.GoverningagenticAIforAFCbeforededicatedguidanceexists

Anti-financialcrimeprogramsareenteringaneffectivenessera.Thedirectionoftravelinthe

UnitedStates—theAMLAct’s1emphasisoninformationthatis“highlyuseful”togovernment,ashifttowardoutcome-basedexamination,andasupervisoryposturethatismovingawayfromprocess-heavy,check-the-boxreview—pushesinstitutionstowardprogramsthatarerisk-basedandmeasuredbyresultsratherthanactivity.Againstthatbackdrop,financialinstitutionsare

adoptingagenticAIininvestigationsandothercomplianceactivitiestostrengthentheirAFCriskmanagementandimprovethequalityoftheirprograms,oftenaheadofanyformalguidanceonhowtogovernthesesystems.Doingsorequiresinstitutionstotranslateestablishedprinciplesofaccountability,validation,oversight,andcontrolintoaformsuitedtosystemsthatcanplan,usetools,andexercisedelegatedauthority.

Thiscreatesapracticalgovernancechallenge.Institutionsmustdeployandoverseea

fundamentallynewtechnologyusingsupervisoryframeworksthatweredevelopedforan

earliergenerationofanalyticalandAIsystems.Existingframeworksinareassuchascyber

security,datagovernance,identityandaccessmanagement,privacy,andoperationalrisk

addressimportantpartsofthischallenge;however,theygenerallydonotprovideanintegratedgovernancemodelfortheend-to-endagenticsystem,includingitsautonomousplanning,tooluse,dynamicworkflows,delegatedauthority,andinteractionwithhumandecision-makers.

Untildedicatedgovernanceframeworksemerge,institutionsneedapracticalapproachforapplyingandextendingtoday’ssupervisoryprinciplestoagenticAI.

Existingmodel-riskguidanceprovidesausefulillustrationofboththeenduringgovernance

principlesandtheboundariesofcurrentframeworks.TheUSinteragencySupervisoryGuidanceonModelRiskManagement,refreshedinApril2026,rescindedandreplacedthelong-standing2011guidance(SR11-7/OCC2011-12)andthe2021interagencystatementonmodelriskfor

BSA/AMLsystems.

1Anti-MoneyLaunderingActof2020(AMLA),whichdirectsthatBSAreportingbe“highlyuseful”togovernmentand

establishesnationalAML/CFTpriorities;subsequentFinCENandinteragencymaterialsreinforcethemovetowardanoutcome-andeffectiveness-basedapproach.

©OliverWyman5

Atthesametime,itexplicitlyrecognizesitsownboundary.Itdefinesa“model”asacomplex

quantitativemethodthatappliesstatistical,economic,orfinancialtheoriestoproduce

quantitativeestimates,anditthenstatesplainlythatgenerativeandagenticAIareoutofscope—whilemakingclearthatthisisnotacontrolexemption:

“GenerativeAIandagenticAImodelsarenoveland

rapidlyevolving.Assuch,theyarenotwithinthescopeof

thisguidance.Nonetheless,abankingorganization’srisk

managementandgovernancepracticesshouldguidethe

determinationofappropriategovernanceandcontrolsforanytools,processes,orsystemsnotcoveredinthisdocument.”

Thispaperstartswiththatpremise.Ratherthanreplacingexistingsupervisoryguidance,it

identifiesthegovernanceprinciplesthatremainapplicableacrosstoday’sframeworksand

introducestheadditionalcontrolsneededtogovernagenticAFCsystemsoperatinginpractice.

ThefundamentalgovernancechallengeagenticAFCintroducesisthatthereisnoinfluenceover,andlittletransparencyinto,thetrainingofthefoundationalmodelatthecoreofthe

agent.Governancethereforehastobebuiltaroundanopaquecore,leveragingelementstheinstitutioncanmanagedirectly:theagent’smandateandoperatinginstructions,itsaccesstodataandtools,theactionsitispermittedtotake,thepointsatwhichhumanapprovalisrequired,andtherecordsneededtoreviewandreconstructitsactivity.

Institutionscandrawonestablishedsupervisory,risk-management,andindustryprinciples,buttheymusttranslatethoseprinciplesintoagovernanceapproachdesignedforthebroaderagenticsystem.BecausetheOCCinteragencymodelriskguidanceprovidesthegovernanceframeworkmostfamiliartomanyUScomplianceandmodelriskteams,weuseitasapracticalorganizingreferenceratherthanasthegoverningframeworkitself.Wealsodrawonbroaderindustryguidance,includingtheWolfsbergGroup’sprinciplesforusingAIandmachine

learninginfinancialcrimecompliance2,andtheFATF’sDecember2025HorizonScanonAIanddeepfakes,whichisnotableforexplicitlynamingagenticAIasanemergingconcernthroughtheAML/CFTlens.3

2WolfsbergGroup,PrinciplesforUsingArtificialIntelligenceandMachineLearninginFinancialCrimeCompliance(December2022).Thefiveelementsarelegitimatepurpose;proportionateuse;designandtechnicalexpertise;accountabilityand

oversight;andopennessandtransparency.

3FATF,HorizonScan:AIandDeepfakes(22December2025)—aforward-lookingreviewofAI-relatedrisksandtrends,includinggenerativeandagenticAI,throughtheAML/CFT/CPFlens.

©OliverWyman6

Throughoutthepaper,weapplyaconsistentthree-lensapproachtoexistinggovernance

frameworks:(1)whichprinciplesremaindirectlyapplicable,(2)whereimportantgapsemergeforagenticsystems,and(3)theadditionalcontrolsneededtoaddressthosegaps.

Thecoreshiftrunningthroughthepaperisthattheunitofgovernancemovesfromthemodeltotheagenticsystem—thatis,themodeltogetherwithitsharness(theorchestrationandtool-callingscaffolding),thedataitcanaccess,theactionsitcantakeorrecommend,thehumansitescalatesto,andtheaudittrailitleavesbehind.

Exhibit1:Frommodeltoworkflow:Theexpandedunitofgovernance

Classicmodel

themodelistheunitofgovernance

Output

score/estimate

Model

transparent:inputs,parametersandlogicaretestable

Inputs

governanceboundary

Agenticsystem

thewholesystemistheunitofgovernance

Charterandprompts

objective—scope—prohibitedactions

Actions/

recommendations

boundedbythecharter

Foundationalmodel

Tools

permissioned,leastprivilege

opaquecore—noinfluenceonortransparencyintotraining

Humanescalation

HITLsign-offbeforeanyconsequentialaction

Dataaccess

approvedsources,retrieval

Audittrail—wrapstheentiresystem

governanceboundary

Source:OliverWymanandHawkanalysis.

Governingthatexpandedunitdependsonanenterpriseagentassurancelayerthroughwhichagents—whetherinternallydevelopedorthird-partyprovided—areregistered,tested,

observed,controlled,andevidenced.

©OliverWyman7

2.DefiningtheagenticAFCsystem

Governancescopeisonlyasclearasthedefinitionofthethingbeinggoverned.Forthispaper,anAFCagentisdefinedasabounded,AI-enabledsystemthatpursuesaninvestigativeor

controlobjectiveacrossapproveddatasourcesandtools.ActivitiesperformedbyAFCagentscaninclude,forexample,gatheringandcross-referencingevidence,summarizingalerts,

sequencinginvestigativesteps,identifyingtypologies,recommendingescalation,drafting

suspicious-activitynarratives,orchallengingthelogicofacase.Weusetheterms“AFCagent”and“agenticsystem”interchangeably.Bothrefertothiscompositesystem—amodeltogetherwithitstools,dataaccess,orchestration,andcontrols—ratherthantoasinglemodel.

AnAFCagentisnotasinglelargelanguagemodelandnotachatbot.Itisbetterunderstoodasasystemofcomponentsassembledtomirroraninvestigator’sworkflow,andcomprisingareasoningmodel,asetoftoolstocallexternalsystemsanddata,amemoryorretrievallayer,andorchestrationlogicthatsequencesthesteps.Thatcompositionispreciselywhytheagentcannotbegovernedasasinglemodel:thebehavioremergesfromtheinteractionofpartsthateachchangeattheirownpace.

Itisworthdistinguishingtheagentfromtwoneighbors,traditionalAIandgenerativeAIwithoutdecisionautonomy,becausethedifferencedrivesthegovernancetreatment:

Table1:ComparisonoftraditionalAI,generativeAIwithoutautonomy,andagenticAI

Dimension

TraditionalAI

GenerativeAI

withoutautonomy

AgenticAI

Corefunction

Predicts,classifies,and

Createscontentin

Plansandexecutesa

scoresstructureddata

responsetoaprompt

sequenceofstepstoreachagoal

Autonomyandaction

Low—followsexplicit

Low—reactstoa

High—decomposes

logic,initiatesnothing

prompt,takesno

independentaction

goals,selectstools,andactsorrecommends

Decisioningspeed

Milliseconds—builtfor

Secondstominutes

Minutestohours—

real-timescoring

orchestratesamulti-stepworkflow

Determinism

High—sameinput

Stochastic—output

Stochasticandpath-

yieldssameoutput

canvary

dependent—behaviorcanvaryacrossruns

Governanceobject

Themodel(inputs,

Themodelplus

Thewholeworkflow:

parameters,output)

theprompt

model+tools+dataaccess+actions+

humanescalation+audittrail

©OliverWyman8

AFCagentsspanawidespectrumofusecasesandautonomy—frominvestigationco-pilotsandfraud-analystsupporttoscreeningdisposition,SuspiciousActivityReports(SAR)drafting,typologyanalysis,exposuremapping,andsystemoptimization.Materialityvariesenormouslyacrosssuchaspectrumintermsofdesign-drivenautonomyandinherentproximitytoa

consequentialoutcome.

Overtime,recurringdesignsalongthisspectrumwilllikelybegintosettleintoasmaller

numberofarchetypes(suchasscreening-dispositionagentorSARdrafter)eachcarryinga

characteristicriskprofileand,byextension,acharacteristiccontrolset.Thedisciplineisstill

emerging,andweexpectinstitutionstodaytoassessandapproveagentsonebyone;however,asportfoliosgrow,recognizingthesearchetypesletscommon,well-understooddesigns

bevalidatedagainstasharedbaselineandapprovedfaster,whilescarcevalidationeffortconcentratesonwhatisgenuinelyneworhigherriskinagivenagent.

Exhibit2:ThespectrumofAFCagentusecasesacrosstheinvestigationlifecycle

CurrentProcess

Batch

processing

Alerts

generated

Analyst

investigation

Transactionoccurs

AgenticAIpre-analystreview

Recommends

orautomates

steps

Summarizes

hit/alert/case

details

Identifiestypologies

Collects

internalandexternaldata

AgenticAIduringandpost-review

Guidereviews

Documentsactions

DraftsSARnarrative

ConductsQ&A

reviewtrains

analyst

AFCagentusecasesacrossthealertandinvestigationlifecycle—before,during,andafteranalystreview.

©OliverWyman9

3.ApplyingexistinggovernanceprinciplestoagenticAFC

Existingsupervisoryguidanceonmodelriskmanagement,horizontalAIgovernance

frameworks,andindustryprinciplesallprovidevaluablegovernanceprinciplesforAI

systems.Althoughtheydifferinscopeandpurpose,theysharecommonthemes:risk-basedgovernance,clearaccountability,validation,ongoingmonitoring,documentation,and

independentoversight.

AgenticAFCsystemsdonotinvalidatetheseprinciples.Instead,theyrevealwhereexisting

frameworksareincomplete.Autonomousplanning,tooluse,dynamicworkflows,anddelegateddecision-makingintroducegovernancequestionsthatarelargelyabsentfromtraditionalmodelgovernanceandonlypartiallyaddressedinbroaderAIgovernanceframeworks.Thechallengeisthereforenottoreplacetoday’sgovernanceframeworksbuttoextendorenhancethemto

encompassthebroaderagenticsystem.

Table2illustratesthisapproachusingtheOCCinteragencymodelriskmanagement

guidanceasapracticalexample.TheOCCframeworkprovidesafamiliargovernancelifecycleformanyfinancialinstitutions,makingitausefulreferencepointfordemonstratinghow

existingprinciplescanbeapplied—andwheretheymustbeenhanced—togovernagentic

AFCsystems.Foreachgovernancecomponent,thetableidentifies(1)theprinciplesthatremaindirectlyapplicable,(2)whereagenticsystemsintroducegovernancegaps,and(3)theadditionalcontrolsneededtoaddressthosegaps.

AgenticAFCbuildsonexistinggovernanceprinciples

butrequirescontrolsthatextendbeyondthemodelitself.Theseshouldaddressaspectsincludingagentautonomy,tooluse,dynamicworkflows,runtimemonitoring,andclearaccountabilityforoutcomes.

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Table2:ExampleofapplyingandextendingOCCmodelriskguidanceforagenticAFC

OCCmodel-

riskcomponent

Similarity/applicabilityforagentic

Gapsinguidanceforagentic

Howtoaddressthegaps

Modeldevelopment,

implementation,

anduse

Defineintended

use,assumptions,

objectives,testing,

anduselimitations—

directlyapplicableto

anagent.

Noconceptof

delegatedauthority,autonomylevel,or

actionsthesystemcantakeorrecommend.

Defineanagentcharter:boundedobjective,autonomy/materialitytier,delegated

authority,escalationlogic,prohibitedactions.

Conceptualsoundness

Itremainsnecessary

toassesswhetherthe

underlyingmodel(s)arewell-designedandfitforpurpose;design-

stagereviewapplies.

Assumesasingle

statisticalmodelwith

stableinputs/outputs;thekeychallengeis

navigatingaroundan

opaquefoundational

model—silenton

prompts,toolselection,orchestration,anddata-accessscope.

Extendthesoundnessreviewtothecharter,instructions/prompts,toolpermissions,

retrievaldesign,andorchestrationlogic.

Ongoingmonitoring

Continuedperformancemonitoringand

processverification

remainessential;driftdetectionapplies.

Staticinput/outputmonitoringmissesbehavioraldrift,

non-determinism,andprompt/tool/modelupdatesbetweenreviews.

Runtimebehavioralmonitoring(pathand

decisionlogging),LLM-as-a-judge,guardrail/exceptionalerting,re-validationtriggeredbymaterialconfigormodelchange—notonlydatadrift.

Outcomesanalysisandexplainability

Comparingoutputsto

expectedoutcomesandexplainingdecisions

remainscore;feature-

levelexplainability(suchasSHAP—SHapley

Additiveexplanations)stillappliesto

anycontributingstatisticalmodels.

Single-decision

analysisdoesnotcovermulti-stepreasoningchains,evidence

lineage,citations,oracombinedhuman

+AIaudittrail.Inner

workingsexplainabilitysuchasSHAPdoes

notapplytothe

opaquefoundationalmodelitself.

Chain-of-thought-levelexplainability;citationscapturedastimestamped,tamper-evident

point-in-timesnapshotsofthesourceas

accessed;auditabilityoftheworkflowpathtaken,includingtheoutcomeateachstep;outcomemetricstiedtoAFCeffectiveness

(suchasSARquality,false-positivereduction).

Governance,policies,andcontrols(roles/authority)

Boardandsenior-

management

accountability,

independentchallenge,andclearrolesare

fullyapplicable.

Doesnotmapdecisionrightsbetweenhumanandagent,ordefine

whoownsagent

behavioracrossAFC/MRMtech/vendor.

DefinehumandecisionrightsandHuman-In-The-Loop(HITL)controls;assignagentownership;alignagentwithhumanintent;extendindependentchallengetoagenticworkflows;bias/fairnesstesting;regulartestingofrobustnessagainstdata-

poisoningattacks.

Modelinventory

Maintainingan

Inventorydesigned

Agent/workflowinventorycapturing

anddocumentation

inventoryand

fordiscretemodels;

components,versions,permissions,and

documentation

anagentcombines

changehistory;treatconfigs/promptsas

isapplicable.

models,tools,prompts,anddataconnections

thatchangeovertime.

versioned,documentedartifacts.

Vendor/

Vendormodel-

Limitedguidanceon

Vendoragentvalidationandcustomization

externalmodels

riskprinciples

validatingvendor

controls;contractualtransparencyontools/

(validation,ongoing

agentswhose

data;ongoingjointmonitoring;customer

monitoring)apply.

orchestrationis

partlyopaque.

retainsactivationanddecisionrights.

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4.Agenticriskandarisk-basedmaterialityframework

IntheAFCdomain,applyingauniformgovernancemodeltoartificialintelligenceis

operationallyinefficientandevenpotentiallydangerous.Subjectingalow-riskinternaladversemediasummarizationutilitytothesamevalidationcycleasatransaction-blockingagent

causesresourcebottlenecksandslowsimplementation.Conversely,applyingloosecontrolstotransactional,high-impactagentsintroducessignificantcomplianceandoperationalexposure.

Todeployagenticsystemssafelyandatscale,institutionsrequirearisk-basedmaterialityframeworkthatcalibratesvalidationandoversighttothesignificanceoftheagent’sactivity.Thisapproachrequiresdistinguishingbetweentwocoreconcepts:

•Riskisaforward-lookingmeasureofuncertainty.Itisafunctionoflikelihood(the

probabilityofsystemfailure,hallucination,orexecutionerror)andimpact(thedownstreamoperational,financial,orregulatoryconsequenceofthatfailure).

•Materialityisanobjective,externalthresholdofsignificance.Anevent,decision,oroutputismaterialifitsoccurrence,omission,ormisstatementwouldinfluencethedecisionsof

humanofficers,auditors,orregulators.

Inanagenticenvironment,materialityservesastheprimaryfilter.Byevaluatingtheinherentmaterialityofthebusinessprocessfirst,governanceteamsestablishtheregulatorybaseline.Ratherthanattemptingtoeliminateallriskfromstochasticmodels,theframeworkuses

materialitytodeterminetherequiredrobustnessofthetechnicalharness.High-materialityusecasesmandatemulti-layereddesigncontrolstoreduceresidualrisk,whilelow-materialityusecasesfollowastreamlinedvalidationpath.

Risk-basedmaterialityshouldreflectboththeinherentexposureoftheusecaseandtheeffectivenessofits

technicalcontrols.Thisenablesinstitutionstoapplyproportionategovernancewithouttreatingallagenticsystemsalike.

©OliverWyman12

4.1Thetripartiterisktaxonomyofagenticsystems

Unliketraditionalquantitativemodels(whicharestaticanddeterministic)orstandalone

generativeAItools(whicharestochasticbuttransactional),agenticAIsystemsintroduce

compoundedexposures.EventhoughtheboundariesbetweenthethreetypesofAIarefluid,conceptuallythesecanbeclassifiedintoatripartiterisktaxonomy:

Classicmodelrisks(thefoundations)

•Sampleanddatabias

•Overfittingandcalibrationerrors

•Conceptanddatadrift

CoregenerativeAIrisks(thecognitivecore)

•Stochasticityandhallucination

•Promptvulnerability(injectionandjailbreaking)

•Context-windowdegradation

Standaloneagenticrisks(theactionlayer)

•Planningfailuresandgoaldrift

•Tool-useandAPIexecutionerrors

•Cascadepropagation(themulti-agentvector)

•Unboundedblastradiusandirreversibility

4.2Thedual-lensmaterialitymodel:Inherentversusdesign-controlled

AcommonpointoffailureinemergingAIgovernanceistheconflationofinherentmaterialitywithdesignchoices,thatisofproblemswithsolutions.Factorssuchas“degreeofautonomy”or“toolaccess”arenotinherenttraitsoftheusecase,butrathertechnicalcontrolsconfiguredbyengineerstomanagerisk.

Toestablishadefensibleframework,institutionsmustevaluateagenticsystemsthroughadual-lensmodel:

©OliverWyman13

Lens1:Pureinherentmateriality(theproblemdomain)

Thefirstlensestablishestheusecase’sinherentmaterialitybeforeconsideringtheeffectivenessoftechnicalorgovernancecontrols.Relevantfactorsareentirelyexternalanddetermined

solelybythebusinessusecaseandoperationalenvironmentbeforeanagentisdesigned.Theycannotbealteredbywritingcode,tuningprompts,oraddingguardrails:

•Valueatrisk(financialstakes).Thecapitalortransactionalvolumethattheprocessdirectlytouches(forexample,reviewinga$100retailtransactionversusmanaginganinstitutionaltreasuryflowof$50,000,000).

•Processcriticality(operationalstakes).Theimportanceoftheprocesstothecontinuedsurvivaloftheinstitution.Afailureincorepaymentroutinghaltsbankinginstantly;afailureinadministrativereportingmerelydelaysaninternalcycle.

•Regulatoryandlegaldensity(compliancestakes).Thestatutoryframeworkgoverningthedomain(suchasBSA/AMLfilingobligations,OFACsanctionscompliance,GDPR,ortheEUAIAct).Aviolationcarriesfixedlegalpenaltiesandregulatorycriticism.

•Environmentalvolatilityandfriction(thereversibilitydomain).Thephysical

characteristicsofthetargetsystem.Modifyinganinternaldatabaseislow-frictionandfullyreversible;writingtoapublicledgerorfilingadocumenttoafederalfinancialintelligenceunitishigh-frictionandpracticallyirreversible.

Lens2:Design-controlledfactors(thetechnicalharness)

Thesecondlensassesseshowthetechnicalharnessconstrainsexposureandreducesresidualrisk.Strongdesigncontrolsmaysupportdeploymentundertighterparameters,butdonotbythemselvesaltertheunderlyingmaterialityoftheproblemdomain.Thesearethearchitectu