Business AI: Auditable, Explainable, Fair, Causal ...€˜Cloud-in-a-Box’ + Solutions = like...
Transcript of 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
Auditable Quantifiable, Explainable, Fair
Trusted Verifiable data and decision lineage
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
Auditable Quantifiable, Explainable, Fair
Trusted Verifiable data and decision lineage
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
ReinforcementLearning
Connect(Data/Model)
Structured&Unstructured
Pastdecisions&payoffs
Desiredbusinessoutcome,KPI,pastdecisions,payoffs
Connectdata/models/SOR/APIs
Workshop(Outcome/KPIs)
Availablecolumns(attributes/labels)relevanttosoughtbusinessoutcome
Shape/formofthedata,models?
Learn(LVE)
Loan2Vec
Explain(AnyModel)
Fair
Audit
Trust
Explain
Wheretoputtheresults– existingSORorAppAPI?
BYO-Model
engineer businessdatascientistanalyst datascientistanalyst datascientistanalystengineer analyst FatBrainApplianceAutomationaudit
F20 Missing Data Quality (88% RB-censoring)
Establish/defineFATEcriteria
Decide(Assist)
Impute
BYO-Model
ReinforcementLearning
Connect(Data/Model)
Structured&Unstructured
Pastdecisions&payoffs
Desiredbusinessoutcome,KPI,pastdecisions,payoffs
Connectdata/models/SOR/APIs
Workshop(Outcome/KPIs)
Availablecolumns(attributes/labels)relevanttosoughtbusinessoutcome
Shape/formofthedata,models?
Learn(LVE)
Explain(AnyModel)
Fair
Audit
Trust
Explain
Wheretoputtheresults– existingSORorAppAPI?
BYO-Model
engineer businessdatascientistanalyst datascientistanalyst datascientistanalystengineer analyst FatBrainApplianceAutomationaudit
F20 AML/Financial Fraud compliance
Establish/defineFATEcriteria
Document2VecConcept2VecChat2Vec
TXN2VecCustomer2VecApplicant2VecAnomaly2Vec
Graph2VecBYO2Vec
Graphs
Text Predict
Classify
Cluster
Decide(Assist)
Predict
BYO-Model
ReinforcementLearning
Connect(Data/Model)
Text
Structured&Unstructured
BYO-Labels
Pastdecisions&payoffs
Desiredbusinessoutcome,KPI,pastdecisions,payoffs
Connectdata/models/SOR/APIs
Workshop(Outcome/KPIs)
Availablecolumns(attributes/labels)relevanttosoughtbusinessoutcome
Shape/formofthedata,models?
Learn(LVE)
Document2VecConcept2Vec
TXN2Vec
Explain(AnyModel)
Fair
Audit
Trust
Explain
Wheretoputtheresults– existingSORorAppAPI?
BYO-Model
engineer businessdatascientistanalyst datascientistanalyst datascientistanalystengineer analyst FatBrainApplianceAutomationaudit
F50 Disconnected Lakes (small data, RL)
Establish/defineFATEcriteria
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
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
Fast start and beyond
• FirstProject:startsmall,withoneprioritizedapp• e.g.,F50BankGlobalRiskAnalyticsDataQualityProject• Outcome™enginedeployedin2weeks(IaaS,4Q17)• Internalinvestigativeteamtrainedonpracticalusecases• TheEngineinproductionyieldingdecision-makingresults• AnnualSaaSlicensesizedbybusinesstransactionvolume
• NextProjectandBeyond:• SameenginedeployedforAMLthenanti-fraud1Q18• Inthepipeline,applicationforGlobalRiskscoring
29
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
OK,canyoupayviacreditcardA
Cashislow,IcannotpaybyphonetodayC
Yes,Icanpaywithcreditcardoverthe
phoneC
ByeC
Hi…..A
Hi…..C
Canyoupaythebalancetoday?A
OK,canyoupayviacreditcardA
OK,canyoupayviacreditcardA
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