Jeff Fluke

Senior Consultant, Underwriting Services

Reden & Anders

Making Predictive Modeling in Renewal Underwriting

Work for You

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Today’s Renewal Approach Why use Predictive Modeling Benefits of Predictive Modeling Opportunities for Underwriting Integration Options Communication Ideas Case Study Q&A

Agenda 8:00 – 9:00AM

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Accuracy Many carriers base small group renewals on loss ratios even though they will agree that the loss ratio of an eight employee group is not credible

ConsistencyMany carriers will have a medical underwriter estimate the ongoing claims; these estimates are rarely consistent between underwriters and sometimes will vary from day to day with the same underwriter

EfficiencyMany carriers will have a medical underwriter determine the diagnosis, prognosis, and projected ongoing amount for each large claimant. This process is frequently very manual and often involves going from one screen shot to the next to obtain the needed information

Concerns with today’s common renewal approaches

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Goal: set the right rate (improve accuracy) Determine underlying health risk of population

Retain and attract good business

Goal: set the right rate (improve consistency) Match premium revenue with expected costs – promote

stability and profit (consistency)

Improve market/employer perception of ability to forecast and manage costs

Increase productivity (improve efficiency) Value-added information produced on a systematic basis –

supports automation and standardization

Value proposition Better information on health risk for individuals and groups

can enhance the underwriting process

Why use Predictive Modeling in Underwriting

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• Enhance the actuarial and underwriting process:• Increase accuracy of forecasts – for new and existing

groups• Improve market perception of ability to forecast and

manage costs• Improve efficiency and productivity of rating process• Compliment or supplement existing tools

Why use Predictive Modeling in Underwriting

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Predictive Modeling - Case ExampleDifferentiating Between Members Patient A. Male, 50, diabetic

– Developed skin ulcers - last month– Most recent HbA1c is 11.0; taken 9 months ago– Documented hypertension, not refilling his prescription– ER visit last month, also had increasing number of visits for the past 3

weeks and seen by 3 different specialists last week– Prior Year’s Cost $4,600

Patient B. Male, 50, diabetic

– Developed skin ulcers – 9 months ago– Most recent HbA1c is 6.3; taken 2 months ago– Documented hypertension, refilling his prescriptions regularly– No recent ER visit, also routine follow-up care – 1 PCP and 1 Specialty

visit in past 3 months– Prior Year’s Cost $5,500

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Case Example

Predicted RisksPatient ARisk Score

Patient BRisk Score

The next 12 Months 11.0 3.5

The next 3 Months 10.0 2.5

Probability of an Inpatient Stay

28 % 6 %

Predicted Cost (next 12 Months)

$33,000 $10,500

Predicted Cost by Services

Inp Out Rx Dr Dx Inp Out Rx Dr Dx

35% 25% 10% 20% 10% 5% 10% 35% 35% 15%

Predicted Risk Output for Patients A and B

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Mr. Wizard’s Science Secrets: This Week – Predictive Modeling

Well Jimmy, the data goes in here, these lights flash on and off for a few minutes. We send the results to actuarial. After that, who knows? Let’s go to a commercial.

-- Don Herbert, TV’s “Mr. Wizard” --

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How do I interpret the weights?

A relative risk of 1.0 = the average person Therefore, a risk score of .70 means that the individual is

only 70% as likely to use healthcare resources than the average person.

A risk score of 37.0 means that the individual is 37 times more likely to use healthcare resources as the average person.

Need to normalize scores and factors to the appropriate risk pool

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Normalizing risk score to rating action

Group Block of Business

Relative risk score .95 .98 A/S factor .90 1.03

Step 1. Normalize the group risk score to the block risk score (group rrs / block rrs)

(.95 / .98 = .97)

Step 2. Normalize the group A/S factor to the block A/S factor(group AS factor / block AS factor)(.90 / 1.03 = .87)

Step 3. Develop the adjusted group risk score(#1 / #2)(.97 / .87 = 1.11)

Adjusted group risk score: 11% higher than the block of business

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Benefits of Predictive Modeling

Streamline group renewal underwriting Automate large claim review process Improved data collection and case preparation More stable underwriting margins Automate reporting capabilities Improved communications with groups/agents

Improved accuracy, consistency, and efficiency

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Small Groups (2 – 50) In states where a health status adjustment is allowed Increased accuracy – area of greatest benefit Automate moving from a risk score to a rating action

Medium Groups (51 – 150) Blended with historic claims to increase accuracy More credibility to the predicted risk vs. prior history

Large Groups (150+) Some blending with historic claims can enhance accuracy Opportunity to determine risk drivers – enhance account

management function

Technical ApproachOpportunities for Underwriting

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Better match premiums to risk – fewer surprises

More Transparent – describe risk drivers

Combine with Care Management programs based upon risk drivers

Integration with current underwriting practices Predictive Modeling results must complement existing information,

including prior experience, credibility assumptions, and other adjusters

Technical ApproachOpportunities for Underwriting - Engaging Employers

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Measure risk for blocks of business– Area

– Broker

– Product

Watch trend/risk over time for book of business– Proactive with future risk score

– Increasing/decreasing – ability to change rating before impacting financial results

– Selection issues

Monitor marketing, sales activities

Technical ApproachAdditional Underwriting/Actuarial Uses

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Added piece of data (macro and micro level) Confirm results from existing process/trends

Support appeals Especially where predicted risk is less than experience

Automated large claim reviews Enhanced ability to identify emerging claims Ease of researching groups and individuals

Part of rating formula Revised credibility table

Integrate into rating process Various levels of automation

Technical ApproachIntegration Options

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Communication Ideas

We are using current technology to improve our ability to assess risk and better match premium to claims

We are using current technology to improve our understanding of medical risk of each renewing group

This improved understanding will allow us to do a better job of setting rates that appropriately reflect the underlying medical risk for each employer

This should increase our retention of lower risk groups and maximize renewal increases on higher risk groups

The member level detail provided by the predictive modeling tool needs to be kept highly confidential

No specific member or group risk scores should be communicated

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Case Study: Integrating PM into Underwriting Process

Integration with current underwriting practice Predictive modeling results must complement existing

information, including prior experience, credibility assumptions, other adjusters.

How can this be accomplished?

Empirical Test Use different models based on prior experience and

Impact Pro risk findings to simulate group premiums.

Compare simulated rates with actual experience -- assess best models.

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Case Study Details

2,557,137 members

85,166 groups

3 health plans Commercial population, mix of products

Primarily non-elderly

Different geographic census regions

30 months of claims and enrollment data (12-6-12)

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Case Study Details

Group Size% of

Members% of

GroupsCost PMPM

Risk Score

1-4 9.8% 73.8% $ 309 1.14

5-9 5.6 11.7 224 0.97

10-19 6.0 6.3 216 0.95

20-49 9.0 4.2 214 0.94

50-99 9.1 1.9 216 0.94

100-249 11.8 1.2 215 0.95

250 and over 48.7 .8 219 1.08

Total 2.56 Million 85,166 $ 227 1.03

Group size based on number of subscribers.

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Case Study: Integrating Predictive Modeling into Underwriting Process

Model Description Features

1 Age/SexRelative demographic risk for those members

active as of the end of the base experience period.

2 1 year Experience Relative prior year’s experience for group

3 Impact Pro RiskImpact Pro relative risk for group

4Risk and

ExperienceCombine Impact Pro risk and prior year’s

experience -- weighted

Models Tested

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Predictive Accuracy – Group R2

00.1

0.20.30.4

0.50.6

0.70.80.9

1

1-4 5-20 20-49 50-99 100-249 250-499 500+

Group Size

A/SexPrior CostImpact Pro2 Tier credibility

12-6-12 scenario using $50,000 threshold. Group R2 describes the % variation in future costs across groups explained by a model.

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Case Study: Integrating Predictive Modeling into Underwriting Process

Weighting of Impact Pro Risk and Experience by Group Size

Employer Group Size Weight for IPro RiskWeight for Prior

Cost

0-4 .88 .12

5-9 .85 .15

20-49 .83 .17

50-99 .75 .25

100-249 .72 .28

250-499 .56 .44

500+ .37 .63

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Predictive Modeling is Very Powerful Information