Prosperity

Insight

SeriesFROMTHEORYTOPRACTICE:A

STRATEGIC

AI

INTEGRATIONMODELFORREVENUEADMINISTRATIONSPublic|

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ININSTITUTIONS|

INSTITUTIONS|INSTITUTIONSINSTITUTIONS|INSTITUTIONSRaúl

Junquera,IvanKrsul,VladimirCalderón,

JoeyGhaleb,andCristianLucasNSTITUTIONS|INSTITUTIONS||

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UT

IONSINFROMTHEORYTOPRACTICE:A

STRATEGIC

AI

INTEGRATIONMODELFORREVENUEADMINISTRATIONSRaúl

Junquera,IvanKrsul,VladimirCalderón,

JoeyGhaleb,andCristianLucasWORLD

BANK

GROUPGOVERNANCEGOVERNANCEGOVERNANCEGOVERNANCEGOVERNANCEGOVERNANC?2025InternationalBankforReconstructionandDevelopment/TheWorldBank1818H

Street

NWWashington

DC

20433Telephone:202-473-1000Internet:

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2625;e-mail:pubrights@.Abstract

·vi1.Introduction

1

·2.

ABriefOverviewof

AI

·

43.Useof

AIinRevenue

Administration

·73.1ObjectivesandGoalsofAIIntegrationintoaRevenue

Administration83.2

PotentialImpactof

AIontheOrganizationalStructureandOperationsof

Revenue

Administrations

93.3TheImportanceofHuman-AICollaboration

·113.4The

Evolutionary

Natureof

AI

Development

123.5ChallengesinImplementing

AI

WithoutaFramework

·144.DataGovernanceanditsRelationtoSyntheticData

·15·5.

A

StrategicFrameworkfor

AIIntegration

·175.1KeyComponentsof

the

Framework

·

185.2FrameworkGenesis

·195.3

Phases

20·

5.3.1Inception

Phase

20··

5.3.2

Consolidation

Phase

22··

5.3.3OptimizationPhase

22·5.4

FrameworkRoadmap

24TABLEOFCONTENTS·6.

UseCases

·

276.1AIin

Tax

Administration

296.2

AIinCustoms

Administration387.Conclusion

·

43·

References

·

45ABSTRACTThispaperpresents

a

comprehensive

strategic

framework

forintegrating

Artificial

Intelligence

(AI)

into

revenue

administrations.

The

framework

addresses

the

challenges

ofimplementing

AI

without

a

structured

approach

and

emphasizes

the

importance

ofhuman-AI

collaboration.

Itproposes

a

three-phase

implementation

strategy—inception,

consolidation,

and

optimization—designed

to

incrementally

build

capacity,

establish

governance

structures,

and

optimize

AI

systems

over

time.Byfollowing

this

framework,

revenue

administrations

can

effectively

harness

the

power

of

AI

to

enhance

efficiency,

improve

taxpayer

services,

and

strengthencomplianceeffortswhilemaintainingpublictrustandtransparency.1.INTRODUCTIONArtificial

Intelligence(AI)hasrapidly

evolved

from

a

theoretical

concept

to

a

practical

reality

in

various

sectors,

including

tax

administration

and

customs.ThelatestOrganisationforEconomic

Co-

operation

and

Development

(OECD)

study

on

Tax

Administration

2024

reveals

that

over

50

percentof

tax

administrations

are

now

using

AI

in

some

capacity,particularlyinareassuchastaxpayer

assistance,

risk

assessment,

and

fraud

detection.

Thiswidespreadadoption

signifies

apivotal

shift

in

how

tax

authorities

operate

and

interact

withtaxpayers.FROMTHEORYTOPRACTICE:ASTRATEGICAIINTEGRATIONMODELFORREVENUEADMINISTRATIONS

ProsperityInsight1But

theimplementation

of

AIinrevenue

administrations

is

not

without

challenges.

Unlike

traditionalsoftwaredevelopment,AIintegration

requires

a

nuanced

approach

that

considers

not

only

technical

aspects

but

also

ethical,

legal,

operational,

and

behavioral

implications.

Revenue

administrationsmustnavigate

complexissues

such

as

data

governance,

model

transparency,

and

the

potentialforbias,allwhilemaintainingpublictrust

and

ensuring

fair

treatment

of

taxpayers.

Another

criticalconsiderationis

the

explainability

of

AI

modelsused

in

tax

administration.

Explainable

AI

(XAI)

refers

to

AI

systems

that

provide

clear,

understandablereasons

for

theirdecisions,

whichisessentialformaintainingtransparency,

fairness,and

trust

in

automated

processes.

Revenue

administrations

need

to

ensure

that

AI-

driven

decisions,

particularly

those

related

to

complianceand

enforcement,

are

interpretable

not

only

by

technical

staff

but

also

by

the

general

publicandtaxpayers.

However,transparencymust

beweighted

againstthe

potential

negative

impact

ofpubliclydisclosingthealgorithm,which

may

result

in

taxpayers′

behavioral

changes

to

avoid

triggering

the

AI

algorithm

parameters

conducive

toaudit.Thepotential

benefits

of

AI

in

government

operations,

including

tax

administration

and

customs,

are

so

significant

that,

in

some

jurisdictions,

thereisnowamandate

toexploreand

implementAI

solutions.

For

instance,

in

the

State

ofCaliforniaintheUnitedStates,the

Governor

has

issuedadecreerequiringallgovernmentagencies,

includingtaxadministrationandcustoms,to

draft

a

report

examining

the

most

significant

and

potentiallybeneficialuse

cases

for

deploying

GenerativeAItools.Thisdirectiveunderscoresthe

growingrecognition

of

AI’s

transformativepotentialin

public

sector

operations

and

the

urgency

with

which

governments

are

seeking

to

harness

these

technologies

toimproveservicedeliveryand

operationalefficiency.

Othergovernment

agencies

arefollowingsuit.Forexample,withPrimeMinister

Giorgia

Meloni

emphasizingAI

as

a

priority

during

Italy’s

G7

presidency,

Italy’s

cabinet

recently

approved

a

bill

to

regulate

AI

use

and

invest

up

to1

billioneurosintheAIsector.While

the

potential

benefits

ofAIin

revenue

administrationsaresignificant,

its

implementationpresents

complex

legal

and

organizationalchallengesthatmustsystematicallybeaddressed.

The

legal

landscapesurrounding

AI

use

in

government

functions,

particularly

insensitive

areas

liketaxadministrationorcustoms,

isstill

evolving.

Tax

authorities

must

navigate

a

complex

web

of

regulationsconcerningdata

privacy,algorithmic

decision-making,andtaxpayer

rights.

As

AI

systems

become

more

integral

to

tax

processes,there

is

a

pressing

need

to

adaptexisting

legalframeworksandpotentiallycreatenewonesto

ensure

that

AIuse

aligns

withprinciplesof

fairness,

transparency,andaccountability.Furthermore,theorganizational

managementof

AIwithinrevenueadministrationspresents

its

own

set

of

challenges.

Traditionally,information

technology

(IT)departmentshave

been

responsible

for

technologicalimplementations.

However,the

pervasive

nature

of

AI,

touching

uponcorebusinessfunctions,legalconsiderations,

andstrategicdecision-making,

requiresa

more

integratedapproach.Revenueadministrations

must

thereforereconsidertheir

organizational

structurestoeffectively

manageAIinitiatives,

ensuringthattheyarealignedwithoverall

strategic

goalsandnotsiloedwithintechnicaldepartments.11.GovernmentsworldwidearedevelopingstrategicobjectivesandinstitutionalarrangementstoensureresponsibleAIdeployment

inthepublicsector,focusingonestablishingcleargovernanceframeworks,securingpoliticalsupport,andmaintainingpublictrust[80];revenueadministrationsareimportantactorsintheseefforts.FROMTHEORYTOPRACTICE:ASTRATEGICAIINTEGRATIONMODELFORREVENUEADMINISTRATIONS

ProsperityInsight2One

significant

challenge

in

revenue

administrationsadoptingAItechnologiesis

the

reliance

on

cloud

services,

which

are

increasingly

necessary

to

handle

large

volumes

of

data

typical

in

tax

administrations.

The

use

of

cloud

services

introducesconcernsregardingdataconfidentiality,

particularly

when

dealing

with

sensitive

taxpayer

information.Manycountrieshavestringentlaws

thatprohibit

storing

orprocessingnational

sensitive

informationoutside

their

jurisdiction

without

propersafeguards.These

restrictionsoften

hinder

revenueadministrationsfrom

fully

leveraging

thescalable,cost-effectivecloudsolutionsofferedby

platforms

like

Google

CloudAI

and

AmazonSageMaker.2Consequently,

development

of

AI

solutions

is

far

from

trivial.

The

successful

integration

of

AI

intorevenueadministrationprocessesrequires

a

strategic

framework

that

allows

for

gradualdevelopment

and

implementation.

Such

a

framework

must

address

key

concerns

including

capacity

building,

data

management,

governance

structures,

and

the

crucial

aspect

of

human-

AIcollaboration.

It

shouldprovidearoadmap

for

revenue

administrations

to

progressively

buildconfidenceinAI

systems,

both

internally

among

staff

and

externally

with

taxpayers

andstakeholders.Thispaper

presents

a

comprehensive

strategic

framework

for

AI

integration

in

revenueadministrations.

The

framework

is

designed

to

guide

tax

authorities

through

three

critical

phases:inception,consolidation,andoptimization.

By

following

this

structured

approach,

tax

administrations

and

customs

can

effectively

harnessthe

powerofAIwhile

mitigating

risks

and

buildingafoundationforlong-termsuccess.2.Itisworth

notingthereareemergingsolutionstothesechallenges.Cloud-native

machine

learning

platforms

nowofferfeatureslikeFederatedLearningandConfidentialComputing,whichalloworganizationsto

benefitfromcloud

infrastructurewhilemaintainingcompliancewithdataprivacylaws.FederatedLearning

enables

multiplejurisdictionstocollaborateonmachinelearningmodelswithoutexchangingrawdata,ensuringthatsensitiveinformationremainswithin

itsoriginal

borders.

ConfidentialComputingensuresthatdataisencryptedevenwhilebeingprocessed,addinganother

layerof

protectionagainstunauthorizedaccess.ThesetechnologiesenablerevenueadministrationstosafelyadoptAIformultijurisdictionaltaxenforcementefforts,enhancingcollaborationandthedetectionoftaxevasionwithoutcompromisingonlegalobligationsrelated

todataprotection.Thesetechnologiesarestillintheearlystagesofdevelopment.FROMTHEORYTOPRACTICE:ASTRATEGICAIINTEGRATIONMODELFORREVENUEADMINISTRATIONS

ProsperityInsight32.ABRIEFOVERVIEWOF

AIArtificial

Intelligence

(AI)

represents

a

revolutionary

field

incomputer

science

that

aims

to

create

systemscapableof

performingtasksthat

typically

requirehuman

intelligence.Thesesystems

processinformation,learn

from

experience,

and

make

decisions

or

predictions

based

on

available

data.Unliketraditionalsoftwarethatfollowspreprogrammed

rules,

AI

systems

can

adapt

and

improve

theirperformance

over

time

through

exposureto

new

information.

This

adaptability

makes

AI

particularly

valuable

in

complex

environments

whererules

are

difficult

to

define

explicitlyorwhereconditionschangefrequently.FROMTHEORYTOPRACTICE:ASTRATEGICAIINTEGRATIONMODELFORREVENUEADMINISTRATIONS

ProsperityInsight4The

foundation

of

modernAI

restsfirmly

on

data,

which

serve

asbothitsfueland

teacher.

High-quality,

comprehensive

datasets

are

essential

for

training

AI

systems

to

recognize

patterns,

make

accurate

predictions,andgeneratemeaningfulinsights.The

relationship

betweenAIanddatais

symbiotic:

the

more

relevant

accurate

data

available,

the

better

theAI

system

can

perform

its

intendedfunctions.

Thisdependencyondata

highlightsthe

critical

importanceofrobustdatagovernanceframeworks

and

data

quality

management

systems

in

any

AIimplementation

strategy[8,26,41,43].Machine

Learning(ML),a

primary

subset

of

AI,

encompasses

various

approaches

to

enable

computerstolearnfromdatawithoutexplicit

programmingandwereinitially

used

to

predict

numericalvaluesfrom

historical

data,

such

as

potential

revenue,

the

risk

exposure

of

taxpayers,

and

so

on.

Deep

Learning,

a

specialized

form

of

MLusingneural

networkswith

multiple

layers,

has

proved

particularly

effective

in

complex

tasks

like

image

recognition,

natural

language

processing

(NLP),andpatterndetection

[41,43].These

neural

networks,

inspired

bythe

human

brain’sstructure,

can

automatically

discover

intricate

patterns

in

large

datasets

and

make

sophisticated

decisions

based

onthese

patterns.

Other

important

MLtechniques

include

supervised

learning,

where

systems

learn

fromlabeledexamples,andunsupervisedlearning,

wheresystemsidentifypatternsinunlabeleddata.Labeling

in

AI

involves

annotating

data

withmeaningful

tags

or

categories

–

for

example,

“fraudulent”vs.“non-fraudulent”transactions

intax

administrations

or“high-risk”vs.

“l(fā)ow-

risk”

shipments

in

customs

–

to

train

models

to

recognize

patternsand

make

predictions.

Labeling

iscrucialasitprovidesthegroundtruththatallowsmodels

to

learn

correlations

and

make

accurate

predictions.

Labeling

is

typically

done

by

experts

manuallyreviewingdata,

usingsemi-automated

tools

toassistinannotation,orleveraginghistorical

datathat

alreadyincludes

classificationsfromauditsorinspections.

However,the

best

anAI

model

can

ever

do

is

to

perfectly

reproduce

the

patterns

inthetraining

data.

So,

if

the

quality

and

accuracyofthetrainingdata

is

poor

(e.g.,

because

humanauditorsinthepasthaveincorrectly

identified

fraudulent

returns

to

audit),

then

this

is

a

fundamental

limitation

on

what

AI

can

do

since

itcan

nevergo

beyond

perfectly

matching

the

human-labeleddata.In

recent

years,

Generative

AI

has

emerged

as

a

transformativetechnologywithinAI,capableof

creating

new

contentsuch

as

images,text,

music,

orevencode.This

category

ofAI,

exemplified

by

models

like

GPT

(Generative

Pretrained

Transformer),

is

capable

of

understandingand

generating

human-like

text

and

other

content

types,thereby

opening

new

possibilities

for

creative

and

operational

efficiency.

Generative

AI

hasbecomeparticularly

impactful

inareas

like

contentcreation,automationof

documentation,

andsimulationscenarios,makingithighlyeffective

for

use

cases

where

generating

new,

contextually

appropriatecontentisrequired[7,10].LargeLanguage

Models

(LLMs),

a

subset

ofGenerativeAI,have

become

powerfultoolsfor

improvingefficiencyinorganizations

liketax

administrationsandcustoms

authorities.

These

advanced

AI

systems,

trained

on

vast

amounts

of

text

data,

can

understand

and

generate

human-like

text[85,86],making

them

highly

effectiveforautomating

complex,

language-basedtasks.

For

example,

in

customs,

LLMs

can

automatically

identify

thecorrectHarmonizedSystem(HS)

code

for

goods

by

analyzing

product

descriptions

orinvoices

[51,

59],

saving

time

and

ensuring

accurateclassification.Intaxadministration,LLMs

can

assist

auditors

by

quickly

extracting

relevant

information

from

large

volumes

of

documents

during

an

audit,

such

as

identifying

discrepancies

in

financial

records

or

pinpointing

noncompliance

with

tax

laws

[79].

By

understanding

context

and

generating

accurate,relevantresponses,LLMshaveFROMTHEORYTOPRACTICE:ASTRATEGICAIINTEGRATIONMODELFORREVENUEADMINISTRATIONS

ProsperityInsight5thepotential

to

transform

operations,

enhancedecision-making,and

improve

compliance

in

revenue

administrations[46,60,78,80].Computer

Vision,

another

crucial

area

of

AI,

enablessystemstounderstandand

process

visual

informationfromtheworld.Thistechnology

hasevolved

from

simpleimage

recognitionto

sophisticatedsystemscapableofreal-time

object

detection,facialrecognition,

and

scene

understanding.

In

parallel,

NLP

focuses

on

enabling

computers

to

understand,

interpret,

and

generate

human

language.These

technologies,

combined

with

advanced

ML

algorithms,

form

the

backbone

of

many

modern

AI

applications,

from

autonomousvehiclesto

intelligent

document

processingsystems.ThefieldofAIalsoencompassesspecializedareas

such

as

Expert

Systems,

which

emulate

decision-

makingprocessesofhumanexperts

inspecific

domains,

and

Robotics,

which

combines

AI

with

physical

systems

to

interact

with

the

real

world.

KnowledgeRepresentationandReasoningsystems

focus

on

storing

information

in

machine-readable

formats

and

using

it

to

solve

complex

problems.

These

specialized

applications

demonstrate

AI’s

versatility

in

addressing

specific

business

and

operationalchallenges.Predictive

Analytics

and

Decision

Support

Systemsare

powerfulAI

applications

that

cansignificantlyenhancetheoperationsof

revenue

administrations

like

tax

and

customs

authorities.

Thesesystemsanalyzehistoricaldatatoanticipate

trends,highlight

risks,

and

guide

decision-

making,

combining

advanced

techniques

such

as

machine

learning,

statistical

analysis,

and

pattern

recognition.Intax

administration,

for

example,

predictive

analytics

can

estimate

futurerevenue

collection

basedon

historicalfilingtrends,

helping

policymakerstosetrealisticbudgetsandplan

resources

effectively.

In

customs,

these

systems

can

identifyshipments

likelyto

contain

prohibited

items

by

analyzing

patterns

in

importer

behavior,

previous

inspections,

and

trade

data.

Their

ability

to

process

and

analyze

large

datasets

in

real-time

empowers

revenue

administrations

to

operate

more

strategically

and

efficiently,improving

complianceandresourceallocation.Recentdevelopments

in

AI

have

also

focused

on

ExplainableAI

(XAI),

which

aims

to

make

AIsystems’decision-makingprocesses

more

transparent

and

interpretable

[73,

87].

This

focus

on

explainability

is

crucial

for

building

trust

and

ensuringaccountability,particularlyinregulated

environments

where

decisionsmustbejustified

and

understood.As

AI

systems

become

more

complexandwidelydeployed,theabilitytoexplain

their

decisions

and

actions

becomes

increasingly

importantformaintainingpublictrust

and

meeting

regulatoryrequirements.FR