business ai: auditable, explainable, fair, causal ...€˜cloud-in-a-box’ + solutions = like...

JointheConversation#OpenPOWERSummit

Business AI: Auditable, Explainable, Fair, Causal - General Cognitive Solutions & Use Cases

RajarshiDas,PhD,CTOandPeterRitz,CEOFatBrainLLC(fatbrain.ai)

Agenda

•  Introductions•  LearnedVectorEmbeddings• Auditability,Explainability,Fairness• PolicyLearning&Causality• UseCases

2

Business AI, Cloud & DevOps

3

Peter B. Ritz Managing Director, Co-founder

Rajarshi Das, Ph.D. CTO, Co-founder Google Scholar link

Soubir Acharya Chief Architect, Co-founder

Shawn Carey Managing Director Field Operations

Problem: you vs. millennial data geometry

4

•  Millennialdatageometry*vs.classicDBs/Analytics

•  AutomatedLearningvs.featureengineering

•  Hyperscalersvs.datatransactionalgravity

•  Solution:BringCognitiveCloud-in-a-BoxtoData

*TermandFigurecourtesyofFatBrain.aiblog-post(2017)DigitalTransformation,InnovationandAIDialectic

Startups

Incumbents

FatBrainClarifaiDataRobot

PalantirClouderaBig4Consulting

Hyper-scaleAISolutions

ProfessionalServices

DigitalReasoningOperaSolutionsH20,Quid…

AmazonGoogleMicrosoft

*BusinessAIformsasignificantpartoftheCognitiveComputingmarketasmorefullyconsideredinFatBrain’spost,id,DigitalTransformation,InnovationandTheAIDialectic

5

Business AI* solutions landscape

Fast Learning business outcomes from data

Auditable Quantifiable, Explainable, Fair

Trusted Verifiable data and decision lineage

FatBrain hyperconverged cognitive solutions

6

Automated Learning business outcomes from data



Connect ODS: TXN, Model, Social data

Learn LVE: cognitive index/backplane

Decide Real-time: life-long, continuous

‘Cloud-in-a-Box’+Solutions=like“Nutanix”forCognitive

PowerAI ICP (optional) DSX (optional)

Orchestrate Bare Metal, Storage, Network, OS

MultiCloud

Spectrum Conductor

Solution A Solution ..

FatBrainSolutions/Engine

CognitiveInfrastructure/Platform

Hype

rcon

verged

Cognitiv

eStack

Connect (Data/Model)

Infer, Predict (Digital Twin)

Learn (LVE)

Explain, Audit (Ledger)

Solution B

•  PowerGPU+SSD+FatBrainSaaS/Solutions•  Setupworkshop+training(1stPOCw/clientdata)

CognitiveAccelerationBundle(startingat$250K):

FatBrain differentiation & solutions

Automated“Vector”LearningforOutcomesReal-time“DigitalTwin”Decision-MakingFatBrainSolutionBlueprints&usecases:•  MissingDataQuality(2-3xbetterimputation)•  AML/FinFraud(4BBTXN/dayà300reports)•  Cross-Sell/Upsell(2%liftinterm/wholelife)•  DeepIntentCustomerCare(20interactionsto2)•  Pharma/PatientCare(75to38daysrevcycle)FastStart:2-weekdeployment*afterworkshop

Fair,Auditable,Trustable,Explainable(FATE)

*Cognitivehyperconvergedappliance,deployableanywhereK8s/Dockerruns MultiCloud

Fast Learning business outcomes from data



Quantifying FATE solutions

Fair•  Principled,transparentanalysisensuresyoucanmakeunbiaseddecisions.

Auditable•  Auto-generatesaledgeraudittrailofhoweverydecisionwasderived.

Trustable•  Verifiablebusinessdecisionstiedtodataandmodel(s).

Explainable•  Quantifiestheefficacyofpossibledecisionstoassistinidentifyingthebestchoice.

Decide(Assist)

Recommend

Predict

Classify

Impute

Cluster

Custom

BYO-Model

ReinforcementLearning

Connect(Data/Model)

Graphs

Text

Structured&Unstructured

Image

Audio

BYO-Labels

Pastdecisions&payoffs

Desiredbusinessoutcome,KPI,pastdecisions,payoffs

Connectdata/models/SOR/APIs

Workshop(Outcome/KPIs)

Availablecolumns(attributes/labels)relevanttosoughtbusinessoutcome

Shape/formofthedata,models?

Learn(LVE)

Document2VecConcept2VecChat2Vec

TXN2VecCustomer2Vec

SKU2VecTaxable2VecApplicant2VecAnomaly2Vec

Loan2VecPatient2Vec

Graph2VecBYO2Vec

Explain(AnyModel)

Fair

Audit

Trust

Explain

Wheretoputtheresults– existingSORorAppAPI?

BYO-Model

engineer businessdatascientistanalyst datascientistanalyst datascientistanalystengineer analyst FatBrainApplianceAutomationaudit

FatBrain solution blueprints workflow (cs 2.0)

Establish/defineFATEcriteria

Decide(Assist)

Impute

BYO-Model


Connect(Data/Model)








Learn(LVE)

Loan2Vec

Explain(AnyModel)

Fair

Audit

Trust

Explain


BYO-Model


F20 Missing Data Quality (88% RB-censoring)


Decide(Assist)

Impute

BYO-Model


Connect(Data/Model)








Learn(LVE)

Explain(AnyModel)

Fair

Audit

Trust

Explain


BYO-Model


F20 AML/Financial Fraud compliance


Document2VecConcept2VecChat2Vec

TXN2VecCustomer2VecApplicant2VecAnomaly2Vec

Graph2VecBYO2Vec

Graphs

Text Predict

Classify

Cluster

Decide(Assist)

Predict

BYO-Model


Connect(Data/Model)

Text


BYO-Labels







Learn(LVE)

Document2VecConcept2Vec

TXN2Vec

Explain(AnyModel)

Fair

Audit

Trust

Explain


BYO-Model


F50 Disconnected Lakes (small data, RL)


Business AI Automation vs. Classic Analytics

13

UsingFatBrain’sBusinessAIAutomationisliketheWazeexperiencefornavigation,insteadofveeringoffandre-directingwithold-fashionmaps

ClassicPredictiveAnalytics BusinessAIAutomationManualfeaturediscoveryandengineering AutomatedlearningthruvectorizedembeddingsMillennialdatageometrydisconnectedfromRDM Learned-policyrealtimedecision-making(RDM)NocommonbackplaneforNLP,regression,etc. Data-shapeandtypeagnosticDomainspecific DomainindependentModel(s)oftenopaque/NA Auditable&explainedManualABPresourceandprocessintensive Anti-BiasPolicy(ABP)enforcement

VS.DIYDIRECTIONSMAP

EXPERIENCE

14

CausationLayer(P=Probability) Activity BusinessOutcomes Example

1.Association

P(y|x)

-Observing-Counting

-Whatis?-HowwouldseeingXchangemybeliefinY?

-Whatdoesasymptomtellmeaboutadisease?-Whatdoesasurveytellusabouttheelectionresults?

2.Intervention

P(y|do(x),z)

-Modeling-TakingAction

-Whatif?-WhatifIdoX?

-WhatifItakeaspirin,willmyheadachebecured?-Whatifwebancigarettes?

3.Counterfactuals

P(yx|x’,y’)

-ActiveLearning-Imagining-Retrospection

-Why?-WasitXthatcausedY?-WhatifIhadacteddifferently?

-Wasittheaspirinthatstoppedmyheadache?-WouldKennedybealivehadOswaldnotshothim?-WhatifIhadnotbeensmokingthepast2years?

Weautomatelearningcausalrelationshipsfrombothunstructuredandstructureddatatodriverealtimedecision-makingtoachieveoutcomes.

Causal inferencing

Learned Vector Embeddings APIs,Data&TeamWorkflows,Architecture,LVE,FATE,RL,Benchmarks

15

Data and APIs workflow*

16

corebanking loan transactions payments marketing

LearnedVectorEmbeddings(LVE)Vectorize{corebanking,loan,… wiredata}

OutcomesEngineaction=recommend{group}

socialmedia model(s)

ConnecttoODSRefine,Life-learn

AlignAPIscopeBusinessoutcome

DataflowKPIse.g.,5BBtxnsà400alerts

Learn

Infer&Explain

1

2

3

Decide

*ReflectingareferenceapproachforAMLandfinancialfraudpreventionwithTop-5globalbank(someimagescourtesyofArcadiaData)

Collaboration and control workflow*

17

Connectdata,SetbusinessKPIs Vectorize,Segment,Analyze1 2

Review,Approve,Deploy3

1frameworkvs.100smodels

ImprovedModelRiskManagement

engineer analyst business datascientistanalyst business analystaudit

*ReflectingareferenceapproachforimprovedModelRiskManagementwithTop-10globalbank

Deployment architecture*

18

① Select/Refine(data)② Vectorize(data+context)③ Learn(distance_metric,lens)④ Persist/BC(vector+context)⑤ API{…},e.g.,recommend()

1

2

4

3

5

datascientist analyst business audit

MultiCloud

*See,AppendixfordetailsonperformancebenchmarksIBMZvs.IBMPowervs.x86

19

Learning behind the scenes GeneralcognitiveapproachshownforDataQualityproject*toauto-learnhiddenrelationshipsin100%ofdata,includingcomplexgroupnetwork,behavioralandtemporalrelationshipsinaquantifiable,auditableandexplainablemanner,revealinghiddenstrategicriskevents.

Missingdata

SamplingStrategyInLearnedLatentSpace

InferredProbabilitydistribution

Imputeddata

Inputvectorspace:

Learnedlatentspace:

Imputedvectorspace:

mean

mean

RED:ClassicLinearR

egression

BLUE:Learned

VectorImpu

tatio

n

*ExcerptedfromF50globalbankDataQualityprojectresults

Transactions Profiles:KYC,CDD,etc.

Sanctions/PEPWatchLists

Rawdata–100%connectedview

Transactions

CoreBankingLoan

Wire

Marketing

SocialMediaCreditCard

Payment

1

Why it works: vectors and fast modeling

20

LearnedVectorEmbeddings(LVE):Automatically*learnlatentvectorrepresentations(wecallLVE)fromanytypeofstructuredorunstructureddata(images,speech,text,numbersoranycombinationofsuchdatatypes)enablingautomatedapplicationofmachinelearning/deeplearningtools.

Raw

Embeddings:5xerrorreductionvs.traditionalfactorization

NLP:Google’sword2vec(wordtovector):wordsrepresentedasnumericalvectorsforlinearalgebraoperationsenablingreasoning,analogy-makingandtranslation.(e.g.,Vector(Human)+Vector(Robot)≅Vector(Cyborg)).Search:GoogleSearchmovedfromrule-basedtolearnedvectorspaceapproachforservingsearchresultsbasedondistancesbetweensearchwordsinvectorspace.NoHadoop-basedcountingtechnologycanachievethatlevelofefficiencyandflexibility(e.g.,seealsorightsidefeaturing10,000xmodelsizereductionfordrugdesigncase)

*See,AppendixfordetailsAICI/CDandfollowingslidesonreinforcementandactivelearning,includingLeSongetal

21

Agivenlearnednon-lineardecisionfunctionf(.)maynotbeexplainedbyagloballinearmodel.WiththeLVEapproachhowever,f(.)canbeexplainedatanypoint✖usingFatBrain’szoomfeaturewhichsamplesinstancesintheneighborhoodof✖,getspredictionsusingf(.),andscoresthembytheproximityto✖ (representedbysize).Thisisequivalenttoestimatingalocalderivativealongeachindependentvariable,eventhoughthedetailsofthemodelf(.)maybeopaque.DashedlineintheClassificationUseCaseisthelearnedexplanationthatislocally(butnotglobally)faithful,whilethebluelineinthefacingRegressionUseCaseisasimilarlearnedregressionexplanation.

Zooming in: Explainable, auditable, fair*

ClassificationUseCase RegressionUseCase

InputFeatures:X1,Categorical

Inpu

tFeatures:X2,Categorical

✖

Y

InputFeatures:X1,Categorical

✖

f(.) f(.)perturbedsamplelocaltopointofInterest,distancereflectedbysize.Forclassification,shaperepresentsclasslabels.Straightdashedlinereflectsinferredlocallinearmodel.

✖

*See,AppendixfordetailsonautomatedFairnesspolicylearning

Explainability

22

TheabovesamplegraphshowsanexampleoftheZoomAPIoutput,wherebyfeatureseachcontributingtoinfluencethemodeloutputfromthebasevalue(theaveragemodeloutputoverthetrainingdataset)tothemodeloutput(24.41).Featuresinfluencingthepredictionhigherareshowninred,whilethoseinfluencingthepredictionlowerareinblue.ThebelowsamplechartbelowshowsZoomAPIoutputforeachdatapoint,rotatedvertically,andthenstackedhorizontally,foranentiredataset

Thenodesoftheabovegrapharethevariablesinaloandatasetandtheedgeweights(thickness)betweenthenodesaredefinedbyvaluesoftheircorrelation.Thenodesizeisdeterminedbyanode’snumberofconnections(nodedegree),nodecolorisdeterminedbyagraphcommunitycalculation,andnodepositionisdefinedbyagraphforcefieldalgorithm.

Fairness

23

BiasExplained(zoom-in)

Originaldecisionboundary(withoutanyconstraints)andtheshifteddecisionboundarythatwaslearnedbythefairclassifier.Noticehowtheboundaryshiftstopushmorenon-protectedpointstothenegativeclass(andvice-versa)

AutoAnti-Baising

ProtectedAttributes(e.g.,sex,race,age)FairnessCriteria,e.g.,avoidfewerqualifiedpeople*inthebluegroupgettingloansvs.theorangegroup.

Caution:Racistbot(MSFT)JusticeSystem(recidivism)

*SomeoftheimagescourtesyofGoogleResearch

Assisted policy learning

ReinforcementLearning(RL):Policy-drivendecision-makingbeyond“actionableinsights”helpsbusinessesmakeandaugmentdecisionsovertime,whilelearningcontinuouslyfrompastdecisionsandtheirconsequences.BusinessAIsystemslearnthruautomatedexperiments,whatnoprogrammer/expertcouldteachthem.

•  MITTechnologyReview:#1BreakthroughTechnologyof2017:ReinforcementLearning,https://www.technologyreview.com/s/603501/10-breakthrough-technologies-2017-reinforcement-learning/,http://videolectures.net/deeplearning2017_sutton_td_learning/

•  Google’sAlphago-ZerobeatAlphago100-0(https://deepmind.com/blog/alphago-zero-learning-scratch/).The“looser”Alphagobeatthehumanworld-championlastyearwhichwashailedasahugeachievementinAI(https://deepmind.com/research/alphago/).

•  SelectgroupofHedgefunds(EngineersGate)foroptimizedtradeexecutions,DarkPoolAllocationproblem(http://www.cis.upenn.edu/~mkearns/papers/rlexec.pdf,http://www.cis.upenn.edu/~mkearns/talks/ICML2006.pdf)

24

HybridRL(SARSA)State-Action-Reward-State-Action

LearnedPolicywithhysterislagBellmanEquationforAction-RewardFunction

(4,4,0)->(3,5,0)

(3,5,0)->(4,4,0)

Learnedvs.HardcodedThrashing

*ImagescourtesyofDasetal.

Biomimetic learning considerations

A

D E

•  CreatingLVEisessentialtolearninggeneralrepresentationofrelationshipstodriveoutcomes,i.e.,businessproblem-specificattributes,inputsorabstractconcepts

•  LVEallowsFatBraintoautomatethecentralprobleminlearning– generalization,thatis:

•  Applyingwhatwasdiscoveredinthepastexperiencestofuturesituationswhicharesimilarinrelevantrespects,

•  Usingthelearnedrepresentation,potentiallyinagenerativefashion,tomakebusinessdecisionstomaximizeabusinessnotionofcumulativereward

•  InstepwithbiomimeticandneuromorphicdesignofDNN,CNNandRL,FatBrain’sLVEparallelsrecentdiscoveriesinneuroscienceonhowhumanbrainorganizesconceptualknowledgewithagrid-likecode*

*See,e.g.,Aronovetal,NATURE:30March2017,Constantinescuetal,SCIENCE:17Jun2016,Tavaresetal,NEURON:1July2015

26

•  ScalabilityofApproximateNearestNeighbors(NNaka“digitaltwin”)

•  Graphrelationshipbetweenquerytime,numberofsamples

•  ComparequerytimewithBruteForcemethod,sameindexsizes

•  Testtwosample/featuresizes–(100k,1M)/(100,1000)

Training & inferencing scalability*

•  Trainingacross3canonicaldatasetscomprisingIMDB(sparse,text),MNISTandCIFAR(dense,numeric)

•  Trainingacross3kindsofneuralnetworks:MLP,CNNandLSTM

•  Quantifyrelationshipbetweentrainingtime&typeofcomputedeployed:z13,z14,Powerandx86

Training Inferencing

*ComprehensiveapproachesmustexaminebothtrainingandinferencingtoprovideapracticalbusinessmeasureforoperatingAI/MLtopologies(weusedTensorFlowfortrainingandSKLearnforinferencing)

EstablishabaselinedeploymenttestbedtooperationalizeCostperBusinessDecisionacrossdifferentinfrastructures

Note:z14usesTensorFlow1.2.1,Powerusesv1.1.0,z13usesv0.10.0,x86usesv1.3.0

Training: IBM z13 vs. z14 vs. x86 vs. Power

IMDBCNN IMDBBidirectionalLSTM MNISTMLP MNISTCNN CIFARCNNz14 55 78 5 70 109

z13 50 71 9 91 224

x86 103 139 4 51 100

Power 6 2 3 16

0

50

100

150

200

250 Trainingtimeinseconds(lowerisbetter)

z14

z13

x86

Power

28

100ksa

mples

1Msa

mples

Inferencing: IBM Z and Power platforms

IBMZ IBMPower

Fast start and beyond

• FirstProject:startsmall,withoneprioritizedapp•  e.g.,F50BankGlobalRiskAnalyticsDataQualityProject• Outcome™enginedeployedin2weeks(IaaS,4Q17)•  Internalinvestigativeteamtrainedonpracticalusecases•  TheEngineinproductionyieldingdecision-makingresults• AnnualSaaSlicensesizedbybusinesstransactionvolume

• NextProjectandBeyond:•  SameenginedeployedforAMLthenanti-fraud1Q18•  Inthepipeline,applicationforGlobalRiskscoring

29

Use Cases DataQuality,AML,DeepIntent,TransactionBehavior,SocialSignals

30

Data Quality challenge

Datashape:1,520,641loanseachcharacterizedby25dataelements,comprising14numericaland11categoricalelements(table)

31

Goal:Imputethedistributionofmissingvaluesfromahybriddataset.

FatBrain AI-augmented Data Quality gains

32

AttributeImpute

% ClassicLinearRegression FatBrainLVE

TestR2

(TrainR2)TestRMSE(TrainRMSE)

TestR2

(TrainR2)TestRMSE(TrainRMSE)

OriginalLoantoValue(LTV)

Mean:70.66

Std:16.86

25% 0.36(0.36)

13.51(13.38)

0.93(0.94)

4.34(4.33)

50% 0.36(0.36)

13.50(13.46)

0.93(0.94)

4.37(4.34)

75% 0.36(0.36)

13.54(13.46)

0.93(0.94)

4.40(4.34)

Debt-to-IncomeRatio(DTI)

Mean:36.30

Std:12.82

25% 0.10(0.10)

12.15(12.14)

0.87(0.88)

4.48(4.46)

50% 0.11(0.10)

12.12(12.17)

0.80(0.86)

4.91(4.61)

75% 0.10(0.10)

12.11(12.16)

0.76(0.86)

5.51(4.63)

mean

mean

RED:ClassicLinearRegression(NFO)BLUE:LearnedVectorImputation(AF)

AML and financial fraud challenge

•  Goal:Improveoperationalefficiency(i.e.,numberoffalsepositivesleadingtoAlertInvestigationwithoutresultinginSAR)intheKYCCareaby3%withastretchgoalof6%.

•  Input:UseavailableSwiftmessagedata,with100%ofthedatafeatures,includingtransactionaldata(type,direction,value),customerdata(geographical,chronological),andriskdata(includingOpenSocialIntelligence).

•  Project:Createaseriesofsegmentsusingasubsetofthedata,keepingthegroupcountconstant,whilecreatingmoreintelligent,defensible,anduniformgroupsusing100%ofavailablefeaturedataviaLVE(topologicallydifferentfromthebank-constructedgroups).

•  Results:Over3ximprovementinoperationalefficiencyoverstretchgoal.UsingLVEapproach,FatBrainengineautomaticallycreatedsegmentationgroups.Thedistributionofcustomerswithinthesegroupswasthenevaluated,independentlyvalidated,anddeployedagainstthebank’sexistinginfrastructure.ImprovedModelRiskManagement.

•  Resources:Three(3)weekswithtwoFatBrainengineersduringsetuptotrainandworkwithtwodomainexpertsfromthebank.Subjecttoitsinternalboardandregulatorapprovalregime,thebankisintheprocessofdeployingtheproposedsolutionglobally.

33

FatBrain AI-augmented AML workflow

34

Connectandlearnfromdata Profilebehavioranomalies1 2

Investigate,learn,report3

Entities:Customers(KYC),Accounts(openandclosedduringasampletimeperiod)

Communications:IPaddress,email,chat,ANI

Transactions:Wires,Posting,CreditCard,Cash

HighRiskLists:Countries,Correspondents,KnownBadActors

DetectedactivitylinkedtoKnownBadActorfromthepublishedlistforYemen-basedfundraising

IP/zipcoderesolutiontoidentifypersonalaccountusagetofunddistributedinternationalwires

Counterpartyenforcementinquiry(highriskcountrycashmovement)

Identifiedotherpreviouslyunrelatedlinkedopenaccounts

Profiledpreviouslyunknownlinked-accountsforanomalousbehavior

Digital-twinawarenessbenchmarkingagainstknowntypologies

AudittrailfordraftSARrecommendation

*ReflectingareferenceapproachforAMLandfinancialfraudpreventionwithTop-5globalbank(someimagescourtesyofArcadiaData)

35

Goal:DistillInteractionsfrom20to2•  AugmentedConversation•  Real-timeIntentQuotient

alignedtobusinessoutcomeorBOM=“LikelytoPay”

•  IQLikelyToPay(“Havenewjob”)=0.8

•  Wouldn’tbenicewhereeveryservicerepresentativehasthepowertoreadthecustomer’smind,inidentifyingtheintentofthecallerfromunstructuredspeech/textin2questionsinsteadof20,&alignittobusinessoutcomes(BOM)?

•  ImaginecallingintoreportbeinglateonaleasepaymentforOctober,becauseyouhaveanewjobwhosepaycheckdoesn’tarriveuntilthenextmonth.Allyouareseekingisareprieveforthecurrentmonth’spayment.

•  Withouttorturingyouwith20differentquestionswiththetypicalvoice-mail-jail,imagineifthebusinesscouldquicklyquantifyyourintentwithrespecttotheBOM=“likelytopay”from2questionsandqualifyyouforthelaterrepayment.

•  Note,norecordsexistinthebackofficethatcustomerhasanewjob,buttheDeepIntentApphelpsidentifyandtransformtheintentofcaller’sspokenrequestintoastructuredeventtoscoreitagainstthedesiredbusinessoutcome.Thatis,wecanquantifythattheintentquotientofBOM=“likelytopay”,giventhecustomer’sinput“haveanewjob”,isquitehigh:IQBO=LikelyToPay(haveanewjob)=0.8

•  Thisinformationisthenrelayedtotheservicerepresentativeinreal-timethroughaugmentedconversationforsuccessfulcustomerengagementforpositivecustomerexperience.

Customer care and engagement challenge

FatBrain Deep Intent workflow

36

Hi…..A

Hi…..C

Canyoupaythebalancetoday?A

OK,canyoupayviacreditcardA


Cashislow,IcannotpaybyphonetodayC

Yes,Icanpaywithcreditcardoverthe

phoneC

ByeC

Hi…..A

Hi…..C

Canyoupaythebalancetoday?A



Cashislow,IcannotpaybyphonetodayC

Yes,Icanpaywithcreditcardoverthe

phoneC

ByeC

A:Youpaybacktoday?C:Ican'tpay(by)thephone

C:OkayperfectyeahIcanpaythat

A:Thepastyearamountyousent.C:****bewithacheckin.

C:YeahIgotpaidtoday

-IQ.UNLIKELYTOPAY

IQ.LIKELYTOPAY

IQ.DIFFERENTIAL

ObtainingCustomerIden2ty&confirmingcallmonitoring

2me

BusinessOutcome:“Likelytopay”

2

1

3

BusinessManagerprovidesaBusinessOutcome,e.g.,“LikelytoPay”totheEngine,tunablewithlearnedinsightse.g.,“move”or“newjob”

WatsonfeedsFatBrainengineunstructuredtranscriptsfromAgent

vs.Customervoicecalls

FatBrainengine

scoresallAgentvs.CustomerconversaGonsin

real-Gmefor

IntentQuo3ent(IQ)quanGfiedbythe“LikelytoPay“

businessoutcome

–personalizedforCausalRewards&Customer2Loyalty

37

“Gaming”transactiondataforhyper-personalexperienceinagivenmonth

O*

O*

O*

OFFERLEGENDLoyaltyOfferDiscountCouponLeaderboardRecognitionOutlierWarning

O*

O*

O* O*

Learning from transactions challenge

GOAL:

38

Individualizedtreatmentpersonas,withcorrespondingprioritytoengage,retainandprovideoffers:Frequencyorvalueofbasketsize.Optimizespendtoreachtheseclientsandkeepthemsatisfied(withoutofferingunnecessarydiscounts),andfindotherslikethem.Keephigh-frequencybuyersengagedwithconsistentmessaging,andtimelyre-targetingtoavoidannoyingseasonalbuyers,infrequentshoppers,andresellers.Loyaltyengagementormobileappdownload:Idealclientsforoptimizationintothehigh-valuecategorywith test promotions, social ads,mobile app downloads, and loyalty programs.Probability of churn: reduce churn byidentifyinglessactivebuyersandofferingtargetedpromotions;re-engageandretainusingemail/chat incentivesforreturnvisitswithpromotions;offerbestpromotionstocustomerswiththehighestpotentialCLTVscore.

Customer Value Frequency Behavior Persona Loyalty Priority Engage:offer Up/XSell

Customer1 $167.25 8 vector Gas&Auto vector stimulatehigherusage mobileapp:Groupon

PhaseA.2

Customer2 $123.70 6 vector LightRetail vector stimulatehigherusage chat:rewardscardoffer

PhaseA.2

Customer3 $228.70 4 vector Groceries vector higherusage chat:instorecoupon

PhaseA.2

Customer4 $671.72 4 vector Collegiate vector maintainpreference,alertforgraduation

call:up-sellforauto-loan

PhaseA.2

Customer5 $1,809.23 16 vector Traveller vector retainrelationshiop email:coupon

PhaseA.2

Customer6 $2,052.06 40 vector Cardascash vector retainrelationshiop download:mobileapp

PhaseA.2

ProvidedData LearnedInsights Actions:Realtime,Personalized,Auto-Curated

FatBrain-enabled cardholder engagement

200+ networks

11,000+ show titles

2M+ show telecasts

TV

Social signals challenge

Social

Online P2P

500M+ tweets/day

340M+ users/mo

50M+ expressions/mo

20M+ reviews

150,000+ views/day

2M+ show telecasts

4B+ searches/day

4.2B+ users/mo

50M+ behaviors/mo

Goal: to deliver a single actionable NPX™ score

NPX GENOTYPE

SCORE

39

FatBrain Theme quantification for social

MinesocialstreamsusingtheThemeProbevectortocapturerealtime+historicalexpressionsforallshowsw/hyperscaleinfrastructure

DevelopademographicprofileofviewerengagementandbrandaffinityfromsocialexpressionsusingLVEtolearnandpredictage,income,showstargetdemowatches

NPX GENOTYPE

SCORE

CreateThemeProbevectorforeveryshowbasedonLVEderivedfrompeertopeerpress(p2pp)e.g.,Reddit,imdb,rottentomatoes,etc.

1

2

3

40

Appendix AICI/CD,BestPractices,PerformanceBenchmarks

41

business ai: auditable, explainable, fair, causal ...€˜cloud-in-a-box’ + solutions = like...

Documents