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RIMS SESSION RIF 010WEDNESDAY, APRIL 30, 2014

2:00 P.M. TO 3:00 P.M.

WORKERS COMPENSATION PREDICTIVE MODELING: THE CRYSTAL BALL BECOMES CLEARER

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TODAY’S PRESENTERS

Melissa Bowman-Miller, StaffmarkDavid Duden, DeloitteSean Martin, TravelersJeff Branca, Marsh

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Today’s Agenda

Introductions & HousekeepingDefining Predictive ModelingRisk Manager’s PerspectiveInsurer’s ViewpointConsultant – Bridging the GapQuestions & Discussion

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What Differentiates Claims Organizations?

5-10%Analytical professionalsCan create new algorithms

Analytical semiprofessionalsCan use visual and basic statistical tools, create simple predictive models

Analytical amateursCan use spreadsheets and use analytical transactions

15-20%

70-80%

Analytical championsLead analytical initiatives1%

“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

H

inds

ight

Ins

ight

For

esig

ht

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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

1 2 3 4 5 6 7 8 9 100%

5%

10%

15%

20%

25%

30%

35%

40%

Pc

t o

f T

ota

l Lo

ss

es

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Rela

tive

clai

m s

ever

ity

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

< 25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65+

-40%

-30%

-20%

-10%

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+

Rela

tive

clai

m s

ever

ity

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

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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

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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

WCSpend

Savings Per $100MOf WC Spend

4% – 8% $100M $4M $8M

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RISK MANAGER’S VIEW

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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 Analysiso Quarterly Roll-Forwards estimating Ultimateso Annual estimates of Pure Premiums (Loss Rates)

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“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

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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)

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MaintainValue

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

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Other Uses of Predictive Analytics

Review of Client/Location PerformanceLoss 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)

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INSURER PERSPECTIVE

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1989 2011p 2019 Estimate

Medicals

In-de

mnity

41%

59%

Med-icals

In-demnity

33%

67%

Workers’ Compensation

Medicals

In-de

mnity53%

47%

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

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Data & Analytics: Predictive Models

The right resources on the right claims

at the right time

Early Identification& Intervention

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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%

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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

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Strategic Claim File Review Selection

Other Factors

Unstructured

Structured

Evolving

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Discussion

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Thank You for Participating