Credit Card Analytics on a Connected Data Platform

CreditCardAnalyticsonaConnectedDataPlatform

2 ©HortonworksInc.2011–2016.AllRightsReservedCopyright©Capgemini2016.AllRightsReserved.

VamsiChemitigantiGeneralManager–FinancialServicesHORTONWORKS

ArindamChoudhuryBigDataPractice

Leader–FinancialServices

CAPGEMINI

Speakers


Agenda

• PaymentsCardIndustryTrends2016

• OurVisionforCreditCardAnalytics

• SolutionArchitecture

• SolutionWalkthrough

• Hortonworks&Cap-GeminiPartnership

• Benefits

• Q&A

4 ©HortonworksInc.2011–2016.AllRightsReserved

StateofthePaymentsCardIndustry


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


BackgroundonPaymentCardFraud


Ã PrimarilyCreditCardFraud– Mushroomingintoaglobalproblem– Problemforconsumers,financialinstitutionsandregulators

– ComplexFraudthatispartofasetofsmallerfrauds

Ã Fraudschemesareincreasinglycomplexandcauseahighlevelofdamage

Ã EMVtechnologyexpectedtoreducePOSfraudinabigway

Ã PaymentcardfrauddetectionplatformsÃ TypesofFraud–

– CNP(Online),Lost,Counterfeit,InterceptedinMail,CardNotReceivedetc

PaymentCardFraud..


TheMagnitudeofPaymentCardFraud


USShareofCreditCardFraud


CurrentApproachesToTacklingFraud


TheFraudITLandscape

Ã FragmentedFraudDetectionSystems

Ã Fragmentedenterprisesystems

Ã Typicallyproprietaryvendorandin-housebuiltsolutions

Ã Overrelianceonpuretransactionmonitoring

Ã Unabletokeeppacewiththeprogressoftechnology

Ã MovetocombineFraud(CreditCardFraud,AML&InfoSec)intooneplatform

Ã Issueswithflexibility,costandscalability


ClassicalApproachtoFraudMonitoringandDetection

Ã RelyingonanExpertSystemsbasedapproach

Ã ManualInvestigationheavy

Ã NewRulesmanuallyaddedasnewschemesoffraudarediscovered

Ã AneedtosupplantandaugmentabovewithamoredynamicapproacharoundDescriptiveandPredictiveAnalyticsMethods


Capgemini&HWXPartnership


Capgemini&Hortonworks partnership

3+yearsofactivecollaboration

• Co-engineering onthemostrecentinnovationstobuildaccelerators andincreasetime-to-value

• Growingandindustrializingcapabilities

• Deliveringsuccessfullymultipleclientengagementsworldwide,includingamonglargestbanksandInsurancecompanies

• Providingseamlesscollaborationandstrongpartneringexperiencetoourclients


OurVisionforCreditCardAnalytics…

§GlobalCreditCardindustryisrapidlychangingandtheparticipantsareincreasinglyfacingnewchallengeswithsnowballingvolumes,regulatorypressuresandnewentrantscompetingforthemarketshare

§ TheCreditCardindustryhasrespondedtothesechallengesbylookingatavenuestocutcosts,increaseefficiencies,providebetter,saferproductsandservicestoattractnewandretainexistingcustomerbase

§Oursolutionisgearedtowardsenhancingthedecisionmakingprocessusingallofthedatathat’savailabletotheCreditCardparticipants,includingcustomerdata,transactions,thirdpartydata,opendata,governmentdata,locationdata,socialdata,etc.

§Withgraphbasedsearchandadvancedanalyticsalongwiththeabilitytohandlelargevolumesthesolutionhelpsyoufindthehiddenpatternsinthedata.Ittellsyouhowyourcustomersbehaveandwhatwillinterestthem.Ittellsyouwhereandwhentotargetthemwithyourpartnersads.Ittellsyouwhichcustomersarelikelytoattrite.Ittellsyouwhoislikelytodefraudyou.IthelpsyoudetectandstopFraudrealtimeandpredictsittoincreaseyourcustomers’experience


…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


…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


SolutionArchitecture


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


MachineLearning:EnterpriseDataScienceatScale

Useflowofdata andthecomputingpoweroftheconnectedplatformtoenableautonomousmachinelearning

• RealtimedataflowscombinedwithmassiveparallelcomputingallowsAItocontinuouslyimprove

• EnablesAItomakedecisionsinthe“GreyAreas”

Build andtrain AIonfullvolumedatanotasample• Time,effort,accuracy,scale• Visualizedataasitisbeingmanipulated

Deploy theAImodelwithoutre-implementing• Sparkmodelscanbepluggedintoamodern

connectedplatform.


SolutionWalkthrough..


ImprovedExperience

/ReducedCost

ImmediateCustomerFeedback

YearsofCustomer

TransactionData

FraudDetection

CompleteCustomerProfile

Realtimeingestof

transactions

Proactivelyidentifypotentialfraudulenttransactionstoprotectthecustomerandimprovecustomerexperience• Proactivelymonitoreverycreditcardtransactionusingmachinelearningtocatchpotentialfraud

• CustomerServiceAnalystreviewsflaggedtransactionsinrealtimeviaanextgenerationapplicationrunningontheconnectedplatform

• HDFcontrolsrealtimeflowofdatainandoutoftheconnectedplatformtothevarioussourceanddestinationpoints

Innovate

Renovate

PurchaseBehaviorInsight

JourneytoFraudDetection


Credit Fraud Analyst Inbox


Fraud Analyst View


Data Flow Design in Hortonworks Data Flow (HDF)


Benefits..


Ã Afreeopensourcelinearlyscalabledata&analyticsplatformhasonlybecomeavailablewithinthelastfewyears

Ã PredictiveAnalytics&DataScienceareanewwayofapproachingFraudDetectionÃ 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?


Q&A


Thankyou!

Credit Card Analytics on a Connected Data Platform

Technology

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