CreditCardAnalyticsonaConnectedDataPlatform
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VamsiChemitigantiGeneralManager–FinancialServicesHORTONWORKS
ArindamChoudhuryBigDataPractice
Leader–FinancialServices
CAPGEMINI
Speakers
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Agenda
• PaymentsCardIndustryTrends2016
• OurVisionforCreditCardAnalytics
• SolutionArchitecture
• SolutionWalkthrough
• Hortonworks&Cap-GeminiPartnership
• Benefits
• Q&A
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StateofthePaymentsCardIndustry
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KeyPaymentIndustryTrendsin2016• PaymentshasbecometheeyeoftheFinTech boom
• DigitalPaymentsincreasinglyreplacingcash
• PaymentsmovingtoanOmnichannelworld
• LoyaltyProgramsgetting increasinglysophisticated
• Increasinglysophisticatedregulations– E.g.PSD2
• NationalPaymentsinfrastructuresmovetorealtime
• NewRevenuemodelstostrengthenfeeincome
• DigitalBanksdrivecostefficiencies
• Moreandmoreplayerswantto“ownthecustomer”
• Paymentsaspartofthecustomerjourney
• Cyberattacks keepincreasing
• Legacy application architecture
– ProductandLOBfocusedvs.CustomerCentric
– Disparatedatasources
– Newdatasources– noabilitytomanage
Omnichannel Payments
Realtime NationalPaymentsinfrastructures
NewRevenueModels tostrengthen income
Digitalbecomingconsumer/Insuredpreferredinteractionchannel– Customer360
Locationbaseddatausage
MovetoRealtimerisk&fraudanalytics
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BackgroundonPaymentCardFraud
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à PrimarilyCreditCardFraud– Mushroomingintoaglobalproblem– Problemforconsumers,financialinstitutionsandregulators
– ComplexFraudthatispartofasetofsmallerfrauds
à Fraudschemesareincreasinglycomplexandcauseahighlevelofdamage
à EMVtechnologyexpectedtoreducePOSfraudinabigway
à Paymentcardfrauddetectionplatformsà TypesofFraud–
– CNP(Online),Lost,Counterfeit,InterceptedinMail,CardNotReceivedetc
PaymentCardFraud..
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TheMagnitudeofPaymentCardFraud
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USShareofCreditCardFraud
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CurrentApproachesToTacklingFraud
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TheFraudITLandscape
à FragmentedFraudDetectionSystems
à Fragmentedenterprisesystems
à Typicallyproprietaryvendorandin-housebuiltsolutions
à Overrelianceonpuretransactionmonitoring
à Unabletokeeppacewiththeprogressoftechnology
à MovetocombineFraud(CreditCardFraud,AML&InfoSec)intooneplatform
à Issueswithflexibility,costandscalability
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ClassicalApproachtoFraudMonitoringandDetection
à RelyingonanExpertSystemsbasedapproach
à ManualInvestigationheavy
à NewRulesmanuallyaddedasnewschemesoffraudarediscovered
à AneedtosupplantandaugmentabovewithamoredynamicapproacharoundDescriptiveandPredictiveAnalyticsMethods
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Capgemini&HWXPartnership
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Capgemini&Hortonworks partnership
3+yearsofactivecollaboration
• Co-engineering onthemostrecentinnovationstobuildaccelerators andincreasetime-to-value
• Growingandindustrializingcapabilities
• Deliveringsuccessfullymultipleclientengagementsworldwide,includingamonglargestbanksandInsurancecompanies
• Providingseamlesscollaborationandstrongpartneringexperiencetoourclients
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OurVisionforCreditCardAnalytics…
§GlobalCreditCardindustryisrapidlychangingandtheparticipantsareincreasinglyfacingnewchallengeswithsnowballingvolumes,regulatorypressuresandnewentrantscompetingforthemarketshare
§ TheCreditCardindustryhasrespondedtothesechallengesbylookingatavenuestocutcosts,increaseefficiencies,providebetter,saferproductsandservicestoattractnewandretainexistingcustomerbase
§Oursolutionisgearedtowardsenhancingthedecisionmakingprocessusingallofthedatathat’savailabletotheCreditCardparticipants,includingcustomerdata,transactions,thirdpartydata,opendata,governmentdata,locationdata,socialdata,etc.
§Withgraphbasedsearchandadvancedanalyticsalongwiththeabilitytohandlelargevolumesthesolutionhelpsyoufindthehiddenpatternsinthedata.Ittellsyouhowyourcustomersbehaveandwhatwillinterestthem.Ittellsyouwhereandwhentotargetthemwithyourpartnersads.Ittellsyouwhichcustomersarelikelytoattrite.Ittellsyouwhoislikelytodefraudyou.IthelpsyoudetectandstopFraudrealtimeandpredictsittoincreaseyourcustomers’experience
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…deliversInsightsforallcreditcardparticipants
Customer720 TransactionsAnalytics
ThirdPartyDataSources
LocationBased
Intelligence
ThirdPartyProcessors
•Frauddetectionandprevention•TransactionpatternbasedFraudPrevention•CustomerbehaviorbasedFraudPrevention•Merchantanalytics•AnalyzeMerchantperformanceamongstpeers•CustomerSegmentation- understandbehaviorsbasedontransactionsfortargetedCampaignManagement•AttritionManagement•CaptureVoiceofCustomerfromSocialMediatomanagechurn•Transactionpatternsbasedchurnmanagement
CardAssociations
•FrauddetectionandpreventionforRetailersandMerchantAcquirers•Macro-economicpredictionsbasedonspendpatterns•MarketingAnalytics•Marketshareanalysis•AnalyticsforRetailers•CustomerBehaviorAnalytics•CustomerSpendAnalytics•CustomerChurnPrediction•AnalyticsforMerchantAcquirers•CreditRiskAnalytics•Merchantperformance(decline)analytics•IoT&PaymentsAnalytics–wearable,RFID,etc.
Issuers
•CustomerAcquisition•CustomerAttritionManagement•Socialmediaintegrationtoidentifyinfluencenetworkandaugmentreferralprograms•Geo-locationbasedpartnerAds./realtimeoffers•“Measure”successofmarketingcampaigns•FraudManagement•RealtimeFraudManagement•LocationbasedFraudManagement•UsageAnalytics•Cardissuedbutnotused– autosmswithintroductoryoffers•Trackdecreaseinusage–providelucrativeoffers
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…utilizesgraphbasedsearchandadvancedanalyticstogatherInsightsandleveragesanintegratedtransactionsandcustomerintelligenceHub
• IntegratewithCustomer720view• Advanced“customeridentification”techniques
• Abilitytoidentify“Cohorts”• Modelsforclusterlinkanalysis
TransactionIntelligence
CustomerIntelligence
CustomizableRules
AnalyticalModelsPaymentsIngestion
SelfLearningModels
BehavioralProfiling
SocialmediaThird-partyIndicators
• Transactionclustering• MultipleMachineLearningAlgos.forpatterndetection
• GatherInsightsfromTransactions
• Clusterandsegmentations• 3rd PartyDataSources• Personas• LinksandRelationships
720CustomerView
Reporting• Feedbackloop forincreasingInsightseffectiveness
• Insightsintegratedwithexistingbusinessprocesses
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SolutionArchitecture
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ReferenceArchitectureforPaymentFraudDetection
Search
Customer and Transactions Store
Fraud ClusterKafka
Unsupervised FeatureExtraction
SocialNetworkAnalysis
Supervised
TrainingSets
MLAlgorithmsPredictiveModels
Pattern Detection & Machine Learning
Payment
Internal Data
3rd Party
Visualization
Historical Transactions
Customer Datasets
Customer 720for Fraud
Hot/Realtime
EnrichmentRules
Warm/StreamingDataFewseconds
<100ms
OfflineDataFewhourstodaystotrainmodelsandgatherInsights
LocalCache
RealTime
TrackingCustomerResponse
SocialMediaStreams
FeedbackModelOutput
Sink
Gateways
APIs
RequesttoTrackrelevantparties
FraudNotification
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MachineLearning:EnterpriseDataScienceatScale
Useflowofdata andthecomputingpoweroftheconnectedplatformtoenableautonomousmachinelearning
• RealtimedataflowscombinedwithmassiveparallelcomputingallowsAItocontinuouslyimprove
• EnablesAItomakedecisionsinthe“GreyAreas”
Build andtrain AIonfullvolumedatanotasample• Time,effort,accuracy,scale• Visualizedataasitisbeingmanipulated
Deploy theAImodelwithoutre-implementing• Sparkmodelscanbepluggedintoamodern
connectedplatform.
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SolutionWalkthrough..
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ImprovedExperience
/ReducedCost
ImmediateCustomerFeedback
YearsofCustomer
TransactionData
FraudDetection
CompleteCustomerProfile
Realtimeingestof
transactions
Proactivelyidentifypotentialfraudulenttransactionstoprotectthecustomerandimprovecustomerexperience• Proactivelymonitoreverycreditcardtransactionusingmachinelearningtocatchpotentialfraud
• CustomerServiceAnalystreviewsflaggedtransactionsinrealtimeviaanextgenerationapplicationrunningontheconnectedplatform
• HDFcontrolsrealtimeflowofdatainandoutoftheconnectedplatformtothevarioussourceanddestinationpoints
Innovate
Renovate
PurchaseBehaviorInsight
JourneytoFraudDetection
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Credit Fraud Analyst Inbox
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Fraud Analyst View
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Fraud Analyst View
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Data Flow Design in Hortonworks Data Flow (HDF)
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Benefits..
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à Afreeopensourcelinearlyscalabledata&analyticsplatformhasonlybecomeavailablewithinthelastfewyears
à PredictiveAnalytics&DataScienceareanewwayofapproachingFraudDetectionà ABigDataPlatformislinearlyandincrementallyscalableandcaneliminatethecostissues
associatedwithrelationalplatforms(e.g.Exadata&Netezza),parallelcomputingplatforms(e.g.Platform&DataSynapseandlargescaledatacachingplatforms(e.g.OracleCoherence&Gemfire:– Nosoftwarelicensingfeesmeanthatthecostisonlyforthegenericbladeanddiskfarms
neededtosupportthedataandinmemorycomputeenvironments.– Thisreducestheupfrontdatabaselicensingandhardwarecostsfromseveralmilliondollars
downtoaroundahalfmilliondollars.– Upfrontparallelcomputeanddatacachingplatformlicensingcostsarereducedfrom
severalmilliontozerodollars.Ã ItcanreplacetheDataWarehouse,theupstreamRisk,FinanceandComplianceOperational
DataStores(ODS’s)andtheBusinessManagedApplications(BMA’s).– EliminatesthecostsassociatedwithdatafeedsandtransformationbetweentheData
WarehouseandthevariousenterpriseODS’sandBMA’s.– EliminatestheredundantITandBusinessstaffthatarerequiredtosupportthefragmented
riskandcompliancesystems.
WhyWillThisWorkNow?
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Q&A
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Thankyou!