jeff fluke senior consultant, underwriting services reden & anders making predictive modeling in...
TRANSCRIPT
Jeff Fluke
Senior Consultant, Underwriting Services
Reden & Anders
Making Predictive Modeling in Renewal Underwriting
Work for You
© Ingenix, Inc. 2
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