1. Page 1 Recording of this session via any media type is strictly prohibited. RIMS SESSION RIF 010 WEDNESDAY, APRIL 30, 2014 2:00 P.M. TO 3:00 P.M. WORKERS COMPENSATION PREDICTIVE MODELING: THE CRYSTAL BALL BECOMES CLEARER

2. Page 2 Recording of this session via any media type is strictly prohibited. TODAY’S PRESENTERS Melissa Bowman-Miller, Staffmark David Duden, Deloitte Sean Martin, Travelers Jeff Branca, Marsh

3. Page 3 Recording of this session via any media type is strictly prohibited. Today’s Agenda Introductions & Housekeeping Defining Predictive Modeling Risk Manager’s Perspective Insurer’s Viewpoint Consultant – Bridging the Gap Questions & Discussion

4. Page 4 Recording of this session via any media type is strictly prohibited. What Differentiates Claims Organizations? 5-10% Analytical professionals Can create new algorithms Analytical semiprofessionals Can use visual and basic statistical tools, create simple predictive models Analytical amateurs Can use spreadsheets and use analytical transactions 15-20% 70-80% Analytical champions Lead analytical initiatives 1% “If we want to make better decisions and take the right actions, we have to use analytics. Putting analytics to work is about improving performance in key business domains using data and analysis.” - Tom Davenport, author of Analytics at Work: Smarter Decisions, Better Results True Claims Predictive Modeling Hindsight Insight Foresight

5. Page 5 Recording of this session via any media type is strictly prohibited. PM Helps Organizations Target High Exposure Claims  When a claimant’s injury is a sprained back, there is a wide and varying distribution of claim outcomes  The worst 20 - 30% of claims contribute to 70 - 80% of loss costs  PM uses a variety of data sources and analytics techniques to enable organizations to predict which claims are most likely to be the worst claims  The graph below shows the varying distribution in total lost days for back sprain injuries Injury: Back Sprain

6. Page 6 Recording of this session via any media type is strictly prohibited. Relative claim severity -80% -60% -40% -20% 0% 20% 40% 60% 80% 100% < 2525-3030-3535-4040-4545-5050-5555-6060-6565+ -40% -30% -20% -10% 0% 10% 20% 30% 40% < 11 to 33 to 55 to 77 to 1010 to 1515 to 2020 to 2525 to 3030+ Relative claim severity Claimant Age Distance: Claimant Home and Employer Insights can be revealed through both traditional and non-traditional risk characteristics. Even use of a relatively small set of predictive variables can enhance claim segmentation. Traditional and Non-traditional Characteristics Can Be Predictive

7. Page 7 Recording of this session via any media type is strictly prohibited. Data From Traditional and Non-traditional Means Used to Predict Outcomes By combining internal data with external data from a number of sources, enhanced segmentation can be achieved. External data can also provide an early indication of existing co-morbidities. Claimant Data Claimant Specific Information Diagnosis Information Years of Employment Type of Work Job Level Average Weekly Wage Claims Data Losses Timing/Patterns Settlement Data Jurisdiction Fraud/Lawsuit Policy History Data Experience Data Policy Data External Public Databases Zip Code Demographic Household Demographic Claimant Medical Legal Medical Data Medical History Treatment History Treating Physician Diagnosis Information Treatment Patterns Prescription Usage Co-morbidities Employer Data Financial Stress Years in Business Public Record Filings Loss Control Data

8. Page 8 Recording of this session via any media type is strictly prohibited.  Right claim, right resource  Improve routing to auto-adjudication  Increase triage consistency through automation Claim Routing & Assignment  Reduce lag time of SIU referrals  Improve mix of claims referred to SIU  Deterrence of “soft-fraud” Fraud Detection  Prompt assignment of nurses on those cases that need it most  Integrate behavior issues into nurse assignment  Cost effective use of field case management Medical Management  Demonstrated ability to close claims faster and cheaper leads to competitive market advantage  Improved client satisfaction strengthens the relationship and brand Top Line Growth Projected Business Impact 5-10% improvement in SIU managed claims 3-7% improvement in nurse managed claims 20-25% redeployment of supervisory resources 4-8% reduction in loss and expense Workers’ compensation models for claim operations are designed to help injured claimants return to work sooner, with reduce loss costs. Clients Are Realizing Significant Benefits From Our Claims Predictive Models Typical Range of Savings for Clients WC Spend Savings Per $100M Of WC Spend 4% – 8%$100M$4M$8M

9. Page 9 Recording of this session via any media type is strictly prohibited. RISK MANAGER’S VIEW

10. Page 10 Recording of this session via any media type is strictly prohibited. Why Predictive Analytics? Workers’ Compensation! Will you be my miracle? Always been metrics focused o Track losses monthly by Business Unit/Branch/Customer – Avg. cost per claim, Loss Rate, Frequency o Annual Workers’ Compensation Actuarial Reserve Analysis o Quarterly Roll-Forwards estimating Ultimates o Annual estimates of Pure Premiums (Loss Rates)

11. Page 11 Recording of this session via any media type is strictly prohibited. “Early Intervention Is the Next Best Thing to Prevention” The power to see the future! If we knew from the start which claims were going to become complex and costly we would: - Assign the claim to the appropriate level adjuster - Increase Management review and involvement - Involve appropriate medical cost control measures - Retain the best legal defense - Focus on Return-to-Work Shout-out to the hard-working Adjuster Not a replacement Tool to help manage claims and reduce workload

12. Page 12 Recording of this session via any media type is strictly prohibited. Data Ex Machina How can predictive analytics be used? …Let me count the ways… Through Scoring (i.e., High (red zone) to Low (green zone)) and action items can provide guidance on: - Claim prioritization - Expedition of low exposure claims - Proper assignment of claims to appropriate level adjuster - Cost effective use of field case management - Loss Reserving - Settlements - Future Allowable estimates (Medicare and Rx Risk) - Subrogation potential - Litigation management - Fraud detection (better utilization of Investigative resources)

13. Page 13 Recording of this session via any media type is strictly prohibited. Maintain Value Proposition Optimize Claim Outcomes Minimize Costs to Handle Maintain Discipline  Deliver high quality service  Connect to customers and agents  Ensure quality medical care  Pay the right amount  Ensure appropriate return to work for all injured workers  Accelerate the claims life cycle  Improve process and operational efficiency  Properly match skills with work  Improve reserve accuracy and consistency  Enhance regulatory and corporate compliance Drive to Excellence  Leadership vision and commitment  Organizational readiness to execute Claim Assignment Medical Case Mgmt. SIU Mgmt. Litigation Escalation A Predictive Model Enables Multiple Business Applications Predictive models prospectively identify adverse claims to enable proactive management strategies across all areas of a claim to drive better business results. Subrogation

14. Page 14 Recording of this session via any media type is strictly prohibited. Other Uses of Predictive Analytics Review of Client/Location Performance Loss Ratio/Frequency Rate by Industry Average (WC Code/State) Tracking locals with higher Loss Ratios Pricing (Lost Cost prediction) Underwriting (Risk Selection and Triggers to ask additional information)

15. Page 15 Recording of this session via any media type is strictly prohibited. INSURER PERSPECTIVE

16. Page 16 Recording of this session via any media type is strictly prohibited. 1989 2011p 2019 Estimate Workers’ Compensation 1 1 Top five states only, normalized by state; includes medical only and indemnity claims. Accident Year evaluated at 24 months. As reported in NCCI State of the Line Report. 2019 Data: Insurance Information Institute

17. Page 17 Recording of this session via any media type is strictly prohibited.

18. Page 18 Recording of this session via any media type is strictly prohibited. Data & Analytics: Predictive Models The right resources on the right claims at the right time Early Identification & Intervention

19. Page 19 Recording of this session via any media type is strictly prohibited. Predictive Models Used on All New Claims Nurse Triage which determines the need for nurse case management Return-to-work Target Dates model identifies expedient and safe return-to- work expectations Subrogation Triage model helps us ensure that we pursue every opportunity for recovery Risk Control Triage model helps determine if it would be beneficial to bring in risk control expertise to help mitigate future, similar risks *Return To Work: National Accounts book of business results for accident year 2012. of injured workers return to work within 30 days with our RTW focus* 67%

20. Page 20 Recording of this session via any media type is strictly prohibited. Models Used During the Life of the Claim Early Intervention Chronic Pain model which helps us manage chronic pain from the beginning of an injury Recidivism model to intervene in claims where re-injury could threaten permanent return-to-work Pharmacy Intervention model targets specific high-risk medications and drug interactions which can harm return-to-work efforts Environmental Scans help us identify and alert claim professionals to state specific variations in the claim handling process

21. Page 21 Recording of this session via any media type is strictly prohibited. Strategic Claim File Review Selection Other FactorsUnstructuredStructured Evolving

22. Page 22 Recording of this session via any media type is strictly prohibited. Page 22 Discussion

23. Page 23 Recording of this session via any media type is strictly prohibited. Page 23 Thank You for Participating