RIMS Session RIF 010 Wednesday, April 30, 2014 2:00 p.m. to 3:00 p.m .

1. WORKERS COMPENSATION PREDICTIVE MODELING: THE CRYSTAL BALL BECOMES CLEARER RIMS Session RIF 010Wednesday, April 30, 20142:00 p.m. to 3:00 p.m.

2. TODAY’S PRESENTERS • Melissa Bowman-Miller, Staffmark • David Duden, Deloitte • Sean Martin, Travelers • Jeff Branca, Marsh

3. Today’s Agenda • Introductions & Housekeeping • Defining Predictive Modeling • Risk Manager’s Perspective • Insurer’s Viewpoint • Consultant – Bridging the Gap • Questions & Discussion

4. What Differentiates Claims Organizations? “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 Analytical champions Lead analytical initiatives 1% True Claims Predictive Modeling Analytical professionals Can create new algorithms 5-10% Hindsight Insight Foresight Analytical semiprofessionals Can use visual and basic statistical tools, create simple predictive models 15-20% Analytical amateurs Can use spreadsheets and use analytical transactions 70-80%

5. 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. Traditional and Non-traditional Characteristics Can Be Predictive 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. 100% 80% Claimant Age 60% 40% 20% Relative claim severity 0% -20% -40% -60% -80% < 25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65+ 40% 30% Distance: Claimant Home and Employer 20% 10% Relative claim severity 0% -10% -20% -30% -40% < 1 1 to 3 3 to 5 5 to 7 7 to 10 10 to 15 15 to 20 20 to 25 25 to 30 30+

7. 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. Medical Data External Public Databases Claimant Data • Medical History • Treatment History • Treating Physician • Diagnosis Information • Treatment Patterns • Prescription Usage • Co-morbidities • Claimant Specific Information • Diagnosis Information • Years of Employment • Type of Work • Job Level • Average Weekly Wage • Zip Code Demographic • Household Demographic • Claimant • Medical • Legal Claims Data Policy History Data Employer Data • Losses • Timing/Patterns • Settlement Data • Jurisdiction • Fraud/Lawsuit • Financial Stress • Years in Business • Public Record Filings • Loss Control Data • Experience Data • Policy Data

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

9. Risk Manager’s view

10. Why Predictive Analytics? Workers’ Compensation! Will you be my miracle? • Always been metrics focused • Track losses monthly by Business Unit/Branch/Customer • Avg. cost per claim, Loss Rate, Frequency • Annual Workers’ Compensation Actuarial Reserve Analysis • Quarterly Roll-Forwards estimating Ultimates • Annual estimates of Pure Premiums (Loss Rates)

11. “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. 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. 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. • Deliver high quality service • Connect to customers and agents • Ensure quality medical care Maintain Value Proposition Claim Assignment • Pay the right amount • Ensure appropriate return to work for all injured workers • Accelerate the claims life cycle • Optimize Claim Outcomes SIU Mgmt. Subrogation • Improve process and operational efficiency • Properly match skills with work Escalation • Minimize Costs to Handle Medical Case Mgmt. • Improve reserve accuracy and consistency • Enhance regulatory and corporate compliance • Maintain Discipline Litigation • Drive to Excellence • Leadership vision and commitment • Organizational readiness to execute

14. 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. Insurer Perspective

16. Workers’ Compensation 1 1989 2011p 2019 Estimate 1Top 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

18. Data & Analytics: Predictive Models Early Identification & Intervention The right resources on the right claims at the right time

19. 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 67% of injured workers return to work within 30 days with our RTW focus* • *Return To Work: National Accounts book of business results for accident year 2012.

20. 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. Evolving

22. Discussion

23. Thank You for Participating