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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Fight Fraud with Big Data Analytics

  1. 1. Fight Fraud with Big Data Analytics this Holiday Season © 2013 Datameer, Inc. All rights reserved.
  2. 2. View Full Recording View the full recording of this webinar at: http://info.datameer.com/SlideshareFighting-Fraud-this-Holiday-Season.html
  3. 3. Fight Fraud with Big Data Analytics this Holiday Season © 2013 Datameer, Inc. All rights reserved.
  4. 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. 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. 6. Fight Fraud with Big Data Analytics this Holiday Season © 2013 Datameer, Inc. All rights reserved.
  7. 7. Agenda •  Current challenges •  What to look for in a solution addressing fraud •  Demo •  Q&A
  8. 8. Challenges Merchants paying $200-250B in fraud losses annually Banks and Financial Organizations losing $12-15B annually eTailers lost $3.5B to online fraud Over 20B credit card transactions annually
  9. 9. Face of Fraud is Changing HELLO my name is $5.15 $3.95 $4.10 $4.15 $4.55 $3.22 greg 7-ELEVEN POS Reports Location Data Transactions Authorizations
  10. 10. APPLICATIONS   Challenges with Existing Data Architecture Custom   Applica4ons   Business     Analy4cs   Packaged   Applica4ons   DATA    SYSTEM   2.8  ZB  in  2012   85%  from  New  Data  Types   RDBMS   EDW   MPP   REPOSITORIES   15x  Machine  Data  by  2020   40  ZB  by  2020   SOURCES   Source: IDC Exis4ng  Sources     (CRM,  ERP,  Clickstream,  Logs)   © Hortonworks Inc. 2013
  11. 11. What to Look For in a Fraud Analytics Solution © 2013 Datameer, Inc. All rights reserved.
  12. 12. Big Data Analytics Lifecycle 1. Integrate Identify Use Case 4. Visualize 2. Prepare 3. Analyze Modern Day Architecture Deploy
  13. 13. Define! ▪ Use Cases ROI and TCO Methodology " Customer Analytics "  ROI customer metrics" " Operational Analytics "  ROI and TCO calculator" " Legacy Modernization " Fraud and Compliance Funnel Optimization Behavioral Analytics Fraud Prevention EDW Customer Optimization Segmentation Increase Customer conversion by 3x Increase Revenue by 2x Identify $2B in potential fraud 98% OpEx savings$1M+ CapEx savings © 2013 Datameer, Inc. All rights reserved. Lower Customer Acquisition Costs by 30%
  14. 14. Polling question 1 © 2013 Datameer, Inc. All rights reserved.
  15. 15. Polling Question What use cases are looking at or implementing today? ▪  Profiling and segmentation ▪  Product development and operations optimization ▪  Cross-sell / up-sell ▪  Campaign management ▪  Acquisition and retention ▪  EDW optimization ▪  Fraud and compliance ▪  Other
  16. 16. Integrate! Codeless Integration Big 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. 17. Prepare and Analyze! Interactive Data Preparation Interactive + Smart Analytics Transparency + Governance " JSON, XML, URL-specific "  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. 18. Visualize! Visualization Anywhere Visual Discovery "   Infographic or dashboard" "   Machine Learning algorithms" "   Run on tablets and smart phone devices" © 2013 Datameer, Inc. All rights reserved.
  19. 19. Deploy! Security Scheduling Monitoring "  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. 20. APPLICATIONS   Modern Data Architecture Enabled Custom   Applica4ons   Business     Analy4cs   Packaged   Applica4ons   DEV  &  DATA   TOOLS   SOURCES   DATA    SYSTEM   BUILD  &   TEST   OPERATIONAL   TOOLS   RDBMS   EDW   MANAGE  &   MONITOR   MPP   REPOSITORIES   Exis4ng  Sources     (CRM,  ERP,  Clickstream,  Logs)   © Hortonworks Inc. 2013 - Confidential Emerging  Sources     (Sensor,  Sen4ment,  Geo,  Unstructured)   Page 20
  21. 21. 3 Requirements for Hadoop Adoption Requirements for Hadoop’s Role in the Modern Data Architecture Integrated Interoperable with existing data center investments Key Services Skills Platform, operational and data services essential for the enterprise Leverage your existing skills: development, operations, analytics © Hortonworks Inc. 2013 - Confidential Page 21
  22. 22. Requirements for Enterprise Hadoop 1 2 3 Key Services Platform, Operational and Data services essential for the enterprise OPERATIONAL   SERVICES   AMBARI   HBASE   CORE   PIG   SQOOP   LOAD  &     EXTRACT   Skills     PLATFORM     SERVICES   Integrated MAP     REDUCE     NFS   TEZ   YARN       WebHDFS   KNOX*   HIVE  &   HCATALOG   HDFS   Enterprise Readiness High Availability, Disaster Recovery, Rolling Upgrades, Security and Snapshots HORTONWORKS     DATA  PLATFORM  (HDP)   Engineered with existing data center investments OS/VM   © Hortonworks Inc. 2013 - Confidential FLUME   FALCON*   OOZIE   Leverage your existing skills: development, analytics, operations DATA   SERVICES   Cloud   Appliance   Page 22
  23. 23. Requirements for Enterprise Hadoop 3 Leverage your existing skills: development, analytics, operations Integration DEVELOP   ANALYZE   2 Skills Platform, operational and data services essential for the enterprise OPERATE   1 Key Services COLLECT   PROCESS   BUILD   EXPLORE   QUERY   DELIVER   PROVISION   MANAGE   MONITOR   Engineered with existing data center investments © Hortonworks Inc. 2013 - Confidential Page 23
  24. 24. Familiar and Existing Tools 3 Leverage your existing skills: development, analytics, operations Integration DEVELOP   ANALYZE   2 Skills Platform, operational and data services essential for the enterprise OPERATE   1 Key Services COLLECT   PROCESS   BUILD   EXPLORE   QUERY   DELIVER   PROVISION   MANAGE   MONITOR   Interoperable with existing data center investments © Hortonworks Inc. 2013 - Confidential Page 24
  25. 25. APPLICATIONS   Requirements for Enterprise Hadoop Custom   Applica4ons   Business     Analy4cs   Packaged   Applica4ons   Integrated with DEV  &  DATA   TOOLS   Applications BUILD  &   DATA    SYSTEM   Business Intelligence, TEST   Developer IDEs, Data Integration SOURCES   3 OPERATIONAL   TOOLS   RDBMS   EDW   MANAGE  &   Systems MONITOR   MPP   Data Systems & Storage, Systems Management REPOSITORIES   Platforms Integration   Exis4ng  Sources   Engineered with Lexisting (CRM,  ERP,  Clickstream,   ogs)   data center investments © Hortonworks Inc. 2013 - Confidential Emerging  Sources     (Sensor,  Sen4ment,  Geo,  Unstructured)   Operating Systems, Virtualization, Cloud, Appliances Page 25
  26. 26. DATA  SYSTEM   APPLICATIONS   Datameer in the Modern Data Architecture DEV  &  DATA  TOOLS   OPERATIONAL  TOOLS   RDBMS   EDW   HANA MPP   SOURCES   INFRASTRUCTURE   Exis4ng  Sources     (CRM,  ERP,  Clickstream,  Logs)   © Hortonworks Inc. 2013 - Confidential Emerging  Sources     (Sensor,  Sen4ment,  Geo,  Unstructured)   Page 26
  27. 27. Demonstration 1 © 2013 Datameer, Inc. All rights reserved.
  28. 28. Identifying Potential Fraud How much has been spent at a vendor? Is that spend normal? Were there transactions… When a credit card stolen?
  29. 29. Identify Outliers in Transactions 1.  Calculate average and standard deviation for each category 2.  Identify outliers in all transactions Transaction Amount - Category Average > 2* Std Dev of Category
  30. 30. Demonstration 2 © 2013 Datameer, Inc. All rights reserved.
  31. 31. Fraud and Data Mining on Hadoop Clustering Column Dependencies Decision Tree Recommendations
  32. 32. Demonstration 3 © 2013 Datameer, Inc. All rights reserved.
  33. 33. Predictive Modeling and Datameer Model Building Model Deployment Integration / Execution PMML                   Datameer Server PMML   PMML   PMML   (models)   (models)   (models)   UPPI  
  34. 34. Predictive Modeling and Fraud 1.  Bring in model 2.  Apply function data to get likelihood transaction is fraudulent
  35. 35. Next Steps: More about Datameer and Big Data www.datameer.com Get started on with Datameer and Hortonworks http://hortonworks.com/hadoop-tutorial/datameer/ Contact us: John Kreisa jkreisa@hortonworks.com Karen Hsu khsu@datameer.com Page 35
  36. 36. Polling Question What part of webinar did you find the most useful? ▪  Use cases ▪  Tool ease of use of setup comparison ▪  Tool quality comparison ▪  Best practices ▪  Demonstration
  37. 37. Q&A
  38. 38. Best Practices © 2013 Datameer, Inc. All rights reserved.
  39. 39. Calculating ROI is a process
  40. 40. Apply ROI to Multiple Projects Project 3 Project 2 Project 1 Hardware Savings Software Savings Productivity Business Benefit
  41. 41. Calculating Return Benefits - Costs = Return Identify Fraud Hardware $$$ Improve Marketing Software Time Increase Sales Integration Flexibility Improve Product People Increase Conversion Operations Lower IT expenses Logistics
  42. 42. Universal Plug-In Overview Features and Model Types The 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) •  Naïve 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

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