fight fraud with big data analytics

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You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season. Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season. In this webinar, you will learn about: current challenges in identifying fraud what to look for in a big data solution addressing fraud how big data analytics can identify credit card fraud best practices

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  • 1.Fight Fraud with Big Data Analytics this Holiday Season 2013 Datameer, Inc. All rights reserved.

2. View Full RecordingView the full recording of this webinar at: http://info.datameer.com/SlideshareFighting-Fraud-this-Holiday-Season.html 3. Fight Fraud with Big Data Analytics this Holiday Season 2013 Datameer, Inc. All rights reserved. 4. About our Speakers Karen Hsu (@Karenhsumar) Karen is Senior Director, Product Marketing at Datameer. With over 15 years of experience in enterprise software, Karen Hsu has co-authored 4 patents and worked in a variety of engineering, marketing and sales roles. Most recently she came from Informatica where she worked with the start-ups Informatica purchased to bring data quality, master data management, B2B and data security solutions to market. Karen has a Bachelors of Science degree in Management Science and Engineering from Stanford University. 5. About our Speakers John Kreisa (@marked_man) A veteran from the enterprise marketing industry John has worked worked on products at every level of the IT stack from the depths of storage through to the insight of business intelligence and analytics. Currently John leads partner and strategic marketing initiatives at open source leader Hortonworks who develops, distributes and supports Apache Hadoop. 6. Fight Fraud with Big Data Analytics this Holiday Season 2013 Datameer, Inc. All rights reserved. 7. Agenda Current challenges What to look for in a solution addressing fraud Demo Q&A 8. Challenges Merchants paying $200-250B in fraud losses annually Banks and Financial Organizations losing $12-15B annually eTailers lost $3.5B to online fraudOver 20B credit card transactions annually 9. Face of Fraud is ChangingHELLO my name is$5.15 $3.95 $4.10$4.15$4.55$3.22 greg7-ELEVENPOS ReportsLocation DataTransactionsAuthorizations 10. APPLICATIONSChallenges with Existing Data Architecture Custom Applica4onsBusiness Analy4csPackaged Applica4onsDATASYSTEM2.8ZBin2012 85%fromNewDataTypes RDBMSEDWMPPREPOSITORIES15xMachineDataby2020 40ZBby2020SOURCESSource: IDCExis4ngSources(CRM,ERP,Clickstream,Logs) Hortonworks Inc. 2013 11. What to Look For in a Fraud Analytics Solution 2013 Datameer, Inc. All rights reserved. 12. Big Data Analytics Lifecycle1. IntegrateIdentify Use Case4. Visualize2. Prepare 3. AnalyzeModern Day ArchitectureDeploy 13. Dene! Use CasesROI and TCO Methodology" Customer Analytics" ROI customer metrics"" Operational Analytics" ROI and TCO calculator"" Legacy Modernization " Fraud and ComplianceFunnel OptimizationBehavioral AnalyticsFraud PreventionEDW Customer OptimizationSegmentationIncrease Customer conversion by 3xIncrease Revenue by 2xIdentify $2B in potential fraud98% OpEx savings$1M+ CapEx savings 2013 Datameer, Inc. All rights reserved.Lower Customer Acquisition Costs by 30% 14. Polling question 1 2013 Datameer, Inc. All rights reserved. 15. Polling Question What use cases are looking at or implementing today? Proling and segmentation Product development and operations optimization Cross-sell / up-sell Campaign management Acquisition and retention EDW optimization Fraud and compliance Other 16. Integrate! Codeless IntegrationBig Data Management"Reuse existing DB views and SQL""Data Partitioning""50+ Datameer connectors, plug-in API""Data Retention policies" 2013 Datameer, Inc. All rights reserved. 17. Prepare and Analyze! Interactive Data PreparationInteractive + Smart AnalyticsTransparency + Governance" JSON, XML, URL-specic" 250+ built-in functions"" Visual data lineage"" Automated machine learning"" Complete audit trail"" SmartSampling "" Metadata catalog"functions" Multi-column joins, unions" 2013 Datameer, Inc. All rights reserved. 18. Visualize! Visualization AnywhereVisual Discovery" Infographic or dashboard"" Machine Learning algorithms"" Run on tablets and smart phone devices" 2013 Datameer, Inc. All rights reserved. 19. Deploy! SecuritySchedulingMonitoring" LDAP / Active Directory "" Dependency triggers"" Monitoring system, jobs," Role based access control"" Data synchronization"" Support for Kerberos"" External scheduling integration"performance, throughput" " Error handling" " Log management" 2013 Datameer, Inc. All rights reserved. 20. APPLICATIONSModern Data Architecture Enabled Custom Applica4onsBusiness Analy4csPackaged Applica4ons DEV&DATA TOOLSSOURCESDATASYSTEMBUILD& TESTOPERATIONAL TOOLS RDBMSEDWMANAGE& MONITORMPPREPOSITORIESExis4ngSources(CRM,ERP,Clickstream,Logs) Hortonworks Inc. 2013 - ConfidentialEmergingSources(Sensor,Sen4ment,Geo,Unstructured)Page 20 21. 3Requirements for Hadoop Adoption Requirements for Hadoops Role in the Modern Data ArchitectureIntegratedInteroperable with existing data center investmentsKey Services SkillsPlatform, operational and data services essential for the enterpriseLeverage your existing skills: development, operations, analytics Hortonworks Inc. 2013 - ConfidentialPage 21 22. Requirements for Enterprise Hadoop1 2 3Key Services Platform, Operational and Data services essential for the enterpriseOPERATIONAL SERVICES AMBARIHBASECOREPIGSQOOP LOAD& EXTRACTSkills PLATFORM SERVICESIntegratedMAP REDUCE NFSTEZYARNWebHDFSKNOX*HIVE&HCATALOGHDFS Enterprise Readiness High Availability, Disaster Recovery, Rolling Upgrades, Security and SnapshotsHORTONWORKS DATAPLATFORM(HDP)Engineered with existing data center investments OS/VM Hortonworks Inc. 2013 - ConfidentialFLUMEFALCON* OOZIELeverage your existing skills: development, analytics, operationsDATA SERVICESCloudAppliance Page 22 23. Requirements for Enterprise Hadoop3Leverage your existing skills: development, analytics, operationsIntegrationDEVELOP ANALYZE2SkillsPlatform, operational and data services essential for the enterpriseOPERATE1Key Services COLLECTPROCESSBUILDEXPLOREQUERYDELIVERPROVISIONMANAGEMONITOREngineered with existing data center investments Hortonworks Inc. 2013 - ConfidentialPage 23 24. Familiar and Existing Tools3Leverage your existing skills: development, analytics, operationsIntegrationDEVELOP ANALYZE2SkillsPlatform, operational and data services essential for the enterpriseOPERATE1Key Services COLLECTPROCESSBUILDEXPLOREQUERYDELIVERPROVISIONMANAGEMONITORInteroperable with existing data center investments Hortonworks Inc. 2013 - ConfidentialPage 24 25. APPLICATIONSRequirements for Enterprise Hadoop Custom Applica4onsBusiness Analy4csPackaged Applica4onsIntegrated with DEV&DATA TOOLSApplications BUILD&DATASYSTEMBusiness Intelligence, TEST Developer IDEs, Data IntegrationSOURCES3OPERATIONAL TOOLS RDBMSEDWMANAGE& Systems MONITORMPPData Systems & Storage, Systems ManagementREPOSITORIESPlatformsIntegration Exis4ngSourcesEngineered with Lexisting (CRM,ERP,Clickstream, ogs) data center investments Hortonworks Inc. 2013 - ConfidentialEmergingSources(Sensor,Sen4ment,Geo,Unstructured)Operating Systems, Virtualization, Cloud, AppliancesPage 25 26. DATASYSTEMAPPLICATIONSDatameer in the Modern Data ArchitectureDEV&DATATOOLSOPERATIONALTOOLS RDBMSEDWHANAMPPSOURCESINFRASTRUCTUREExis4ngSources(CRM,ERP,Clickstream,Logs) Hortonworks Inc. 2013 - ConfidentialEmergingSources(Sensor,Sen4ment,Geo,Unstructured)Page 26 27. Demonstration 1 2013 Datameer, Inc. All rights reserved. 28. Identifying Potential FraudHow much has been spent at a vendor?Is that spend normal?Were there transactionsWhen a credit card stolen? 29. Identify Outliers in Transactions 1. Calculate average and standard deviation for each category2. Identify outliers in all transactions Transaction Amount-Category Average> 2*Std Dev of Category 30. Demonstration 2 2013 Datameer, Inc. All rights reserved. 31. Fraud and Data Mining on Hadoop ClusteringColumn DependenciesDecision TreeRecommendations 32. Demonstration 3 2013 Datameer, Inc. All rights reserved. 33. Predictive Modeling and Datameer Model BuildingModel Deployment Integration / ExecutionPMML Datameer Server PMML PMML PMML (models) (models) (models)UPPI 34. Predictive Modeling and Fraud 1. Bring in model2. Apply function data to get likelihood transaction is fraudulent 35. Next Steps: More about Datameer and Big Data www.datameer.comGet started on with Datameer and Hortonworks http://hortonworks.com/hadoop-tutorial/datameer/Contact us: John Kreisa [email protected] Karen Hsu [email protected] Page 35 36. Polling Question What part of webinar did you nd the most useful? Use cases Tool ease of use of setup comparison Tool quality comparison Best practices Demonstration 37. Q&A 38. Best Practices 2013 Datameer, Inc. All rights reserved. 39. Calculating ROI is a process 40. Apply ROI to Multiple ProjectsProject 3 Project 2 Project 1 Hardware SavingsSoftware SavingsProductivityBusiness Benet 41. Calculating Return Benets-Costs=ReturnIdentify FraudHardware$$$Improve MarketingSoftwareTimeIncrease SalesIntegrationFlexibilityImprove ProductPeopleIncrease ConversionOperationsLower IT expensesLogistics 42. Universal Plug-In Overview Features and Model TypesThe Plug-in delivers a wide range of predictive analytics for high performance scoring, including: Decision Trees for classification and regression Neural Network Models: Back-Propagation, Radial-Basis Function, and Neural-Gas Support Vector Machines for regression, binary and multi-class classification Linear and Logistic Regression (binary and multinomial) Nave Bayes Classifiers General and Generalized Linear Models Cox Regression Models Rule Set Models (flat decision trees) Clustering Models: Distribution-Based, Center-Based, and 2-Step Clustering Scorecards (including reason codes) Association Rules Multiple Models: Model ensemble, segmentation, chaining and composition It also implements the a data dictionary, missing/invalid values handling and data pre-processing.42