1. Loss Prevention, Auditing & Safety Conference 2009 Title Sponsor:

2. Improving Organizational Safety Through Predictive Modeling Kris Russell – Sr. Manager Risk Strategy Insights, Research & Analysis Wal-Mart Stores Inc.

3. Agenda  Introduction  Predictive Modeling Defined  Predictive Modeling in the Retail World  What to Expect  Summary/Conclusion  Q&A

4. Agenda  Introduction  Predictive Modeling Defined  Predictive Modeling in the Retail World  What to Expect  Summary/Conclusion  Q&A

5. Predictive Modeling Defined  Predictive Modeling – *Deloitte’s Definition  Data Mining  Algorithms  Segmentation  Vulnerable Store Identification  Focused Resource Deployment *Deloitte Touche Tohmatsu

6. How Predictive Modeling Works  Score Indicator

7. How Predictive Modeling Works  Score Based Groups  Skill Matches Group  Better Initial Assignment

8. Predictive Modeling Concept – Example  Early ID Benefit  Prevention vs. Prosecution

9. Agenda  Introduction  Predictive Modeling Defined  Predictive Modeling in the Retail World  What to Expect  Summary/Conclusion  Q&A

10. Wal-Mart’s Predictive Modeling Philosophy  Combine Multiple Models  Produce Consolidated Score  Overall Claims Evaluation

11. Wal-Mart Litigation Model Case Study  Traditional Process  Random  Time Consuming  Goal: Flag High Potential Claims  Claim Opening + 30 Days  Identification Claim Management

12. Wal-Mart Litigation Model Case Study  Uses 26 variables  The Question: Science = Experience?  Outcome: ‘Lift’ in Identification

13. Wal-Mart Litigation Model Case Study  ‘Lift’?  Traditional Approach ->  Predictive Modeling ->

14. Wal-Mart Litigation Model Case Study  Adjuster 1: 14 Years Experience  7 of 25  Adjuster 2: 25 Years Experience  6 of 25  Model  7.5* of 25

15. Agenda  Introduction  Predictive Modeling Defined  Predictive Modeling in the Retail World  What to Expect  Summary/Conclusion  Q&A

16. Predictive Modeling Life Cycle Data Understanding Data Preparation ModelingEvaluation Deployment Business Understanding

17. Data Approach Choices  Decentralized vs. Centralized  Ownership of data

18. What to Expect  Data is Key  Ask For Help  Use an experienced actuary

19. Agenda  Introduction  Predictive Modeling Defined  Predictive Modeling in the Retail World  What to Expect  Summary/Conclusion  Q&A

20. Summary/ Conclusion  Predictive Modeling Proactive Data Use  Improved Resource Allocation  Narrow the Window  Data is Power

21. Agenda  Introduction  Predictive Modeling Defined  Predictive Modeling in the Retail World  What to Expect  Summary/Conclusion  Q&A