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New Developments in Predictive Modeling
Jonathan Shreve, FSA, MAAA Principal and
Consulting Actuary Milliman
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SOMMAIRE/ SUMMARY
Overview Optimal Use of Risk
Adjusters Lifestyle-Based Prediction

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• Predictive Modeling
• Methods to predict expected claims costs
• Uses historical data and calibrated models
• Many uses in health insurance context
• Renewal underwriting
• Cost impact modeling
• Payment equalization
• Care management

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• Health Insurance Market in the United States
• Individual
• Small group (2-50 employees)
• Medium group (51-500 employees)
• Large group (gt 500 employees)

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• Risk Adjusters Overview
• Risk adjusters measure morbidity
• Used for adjusting payments (Medicare),
  predictive modeling (SG rating), and medical
  management (DM)
• Function of age, gender, and claim history
  (diagnoses and services - medical and/or Rx).
• ERG, ACG, DxCG, etc.

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• Risk Adjusters Overview
• Claim detail is sorted and formatted
• Software assigns members to relatively broad
  diagnosis categories (e.g. Symmetry has 120
  categories called Episode Risk Groups (ERGs))
• Output file (array) of 0s and 1s under each
  demographic category and each condition category
  for each member
• Regression to fit actual costs to array of 0s and
  1s
• Other risk adjusters

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• Risk Adjusters Theoretical Value

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• United States Small Group Underwriting
• Small group rating
• Health insurance coverage
• Small group 2 to 50 employees
• Guaranteed Issue
• Limits on rate adjustments due to health status
• Limits on rates offered to different groups

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• Introduction Real World Considerations
• Delay between when rates are developed and the
  rating period
• Incomplete data (IBNR)
• Rating limits (total Health Status Factor and
  changes)
• Turnover
• Competing against carriers new business methods,
  not their renewal methods

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• Introduction Prior Studies
• Society of Actuaries Report (May, 2002 Cummings
  et al)
• Society of Actuaries Health Section Council
  Article (Aug, 2003 Ellis - DxCGs)
• Society of Actuaries Report (Summer 2006)

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• Society of Actuaries Assessment of Available
  Claims Based Predictive Modeling/Risk Adjuster
  Tools
• Objective analysis of predictive power of
  commercially available risk adjusters
• Updates 2002 study
• Measures , MAPE, and grouped statistics
  (including fit within disease category)

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• Society of Actuaries Assessment of Available
  Claims Based Predictive Modeling/Risk Adjuster
  Tools
• Vendors/Products Included
• Company Product
• Ingenix Episode Risk Groups (ERGs)
• Ingenix Pharmacy Risk Groups (PRGs)
• Ingenix Impact Pro
• Johns Hopkins Adjusted Clinical Groups (ACGs)
• UCSD, Todd Gilmer Medicaid Rx
• MedAI MedAI
• DxCG Diagnostic Cost Groups (DCGs)
• DxCG RxGroups
• DxCG Underwriting Models
• 3M Clinical Risk Groups

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• Society of Actuaries Assessment of Available
  Claims Based Predictive Modeling/Risk Adjuster
  Tools
• Biggest changes from prior study
• New tools (i.e. MedAI)
• Improvement in tools
• Use of prior costs in some models
• Results with data lag

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• Publicly Available Risk Adjusters
• Medicaid Rx
• RxRisk
• CDPS
• Information from 2002 Study A Comparative
  Analysis of Claims-Based Methods of Health Risk
  Assessment for Commercial Populations,
• Cumming/Knutson/Cameron/Derrick
• Some restrictions on use may exist

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• Publicly Available Risk Adjusters
• Medicaid Rx
• Pharmacy based risk assessment model developed by
  Todd Gilmer and other at Univ. of California
• Assigns each member to one or more of 45
  condition categories based on prescription drugs
  used
• Assigns each member to one of 11 age/gender
  categories
• Predicts overall costs for each member
• Includes separate sets of weights for adults and
  children

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• Publicly Available Risk Adjusters
• Rx Risk
• Pharmacy based risk assessment model developed by
  Paul Fishman at Group Health Cooperative of Puget
  Sound
• Assigns each member to one or more of 27 medical
  condition categories for adults, and up to 42 for
  children
• Assigns members to one of 22 age/gender
  categories
• Predicts total medical costs for each member

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• Publicly Available Risk Adjusters
• CDPS (www.medicine.ucsd.edu/fpm/cdps)
• Diagnosis based risk assessment model developed
  by Richard Kronick and others at the Univ. of
  California
• Orignally intended for use with Medicaid,
  including disabled and Temporary Aid for Need
  Familites (TANF) populations
• Assigns members to up to 67 possible medical
  condition categories
• Assigns members to one of 16 age/gender
  categories
• Predicts total medical costs
• Model contains different sets of weights for
  adults and children

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• Milliman Research
• Optimal Renewal Guidelines
• Goal of Research
• Understand current small group renewal practices
• Identify optimal renewal methodologies

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• Introduction Survey Results
• What methods are currently practiced to rate
  small groups at renewal?
• Surveyed 21 carriers on SG methods
• 30 of carriers used risk adjusters
• 60 of groups

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• Introduction Main Components
• Individualized Data Analysis
• Carrier Analysis
• Competitive Simulation

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• Introduction Individualized Data
• Large multicarrier database used to review
  individual predictions
• Advantages
• Large database
• Good geographical representation
• Disadvantages
• No group identifiers
• Manual rate unavailable

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• Introduction Carrier Data
• Advantages
• Actual Group Data
• Group Manual Rates Available
• Disadvantages
• Medium sized data set
• Geographical concentration
• Biased

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• Models Loss Ratio Model
• 1st Renewal
• 2nd Renewal

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• Models Risk Adjuster Model
• 1st Renewal
• 2nd Renewal

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• Models Service Category Model
• 1st Renewal

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• Results Error Measures
• R-Squared - of variance from the mean explained
  by rating variables
• MAPE - Absolute error as of total costs

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• Results Theoretical

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• Results Error Calculation Example
• Small Group ABC
• Traditional Prediction 150
• Risk Adjuster Prediction 125
• Actual Claims equal 120 of manual
• Which method is better?
• Error / R-squared?

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• Results Credibility Weights
• 1st Renewal, Individual Analysis

Svc category 2 IP, 24 OP, 18 Rx
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• Results R-square
• R-Square vs. Rating Caps (Group Size 10)

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• Results Mean Absolute Prediction Error (as )
• MAPE vs. Rating Caps (Group Size 10)

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• Results Mean Absolute Prediction Error (as )
• MAPE vs. Group Size (Rating Cap 35)

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• Results Mean Absolute Prediction Error (as )
• MAPE vs. Group Size (Uncapped)

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• Results Carrier Analysis
• Real groups
• Turnover
• Biased sample
• Traditional / Risk Adjuster very similar!
• Health status correlation

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• Competitive Simulation Introduction
• Based on carrier data
• Excel model - stochastic
• First renewal with 9 months of historic claims.
• New business method accuracy simulated relative
  to renewal method accuracy (less accurate)
• New business quotes generated stochastically
  (Bayesian from renewal quote distribution) with
  some correlation among different carriers

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• Competitive Simulation Results
• Small improvements in new business methods
  significantly increase profitability for new
  business and hurt profitability for renewal
• Very sensitive to point at which group seeks new
  business quotes (try to keep your groups from
  getting quotes!)
• Number of competing quotes is important.
• Accuracy and results are sensitive to credibility
  of risk adjuster and/or historic experience
  components

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• Research Conclusions
• Marginal value of improvements decrease as
  allowable rate variation decreases, and as group
  size increases
• New business is less profitable than renewal
  business. Dont chase the wrong groups away.
• Competitive results are very sensitive to
  accuracy of new business methods
• Credibility is affected by accuracy / explanatory
  power of manual rate and level of health status
  correlation

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• Recommendations
• Understand effects of rating environment
• Fundamentals (Blocking Tackling)
• Objectively analyze what prediction method is
  right for you. It may be that multiple methods
  are most appropriate (state, group size, costs,
  etc).
• Use all relevant data / information on a group.
• Understand what your competitors are doing with
  new business
• Assign credibility explicitly and carefully.
• Use a rigorous, systematic method to develop
  renewal quotes, with appropriate, efficient
  manual intervention.
• Capture all information on each renewal quote and
  what happens with group. Analyze data and modify
  your approach.

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• Lifestyle-Based Prediction

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• The US Surgeon General
• 70 of the diseases and subsequent deaths
• in the U.S. are lifestyle-based
• The Centers for Disease Control
• Lifestyle-based chronic diseases account
• for 75 of the United States 1.4 trillion
  medical care costs

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• Definition of Lifestyle Diseases
• Lifestyle diseases (also called diseases of
  longevity or diseases of civilization) are
  diseases that appear to increase in frequency as
  countries become more industrialized and people
  live longer. (WHO)
• Lifestyle disease is a disease associated with
  the way a person or group of people lives.
• Lifestyle diseases include atherosclerosis, heart
  disease, and stroke obesity and type 2 diabetes
  diseases associated with smoking, alcohol, and
  drug abuse. Regular physical activity helps
  prevent obesity, heart disease, hypertension,
  diabetes, colon cancer, and premature mortality.
  
• (Stedmans Medical
  Dictionary)

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• Lifestyle-Based Diseases
• Lifestyle-Based Diseases/Conditions
• Diabetes
• Hypertension
• Cardiovascular
• Stroke
• COPD
• Most cancers
• Some mental health Depression, Alzheimers, etc.
• Others Osteoporosis, Arthritis, Back Pain, etc.
• Maternity

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• Lifestyle-Based Diseases
• Correlation between Lifestyle and Cancer

Source American Cancer Society
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• 2004 INTERHEART Study
• Over 90 of the risk of a heart attack
  (myocardial infarction) is attributed to
  lifestyle factors
• Factors include abnormal lipids, smoking,
  hypertension, abdominal obesity, consumption of
  fruits and vegetables, alcohol and regular
  physical activity
• Family history thought by many to be the major
  risk, only accounts for 1 of the population
  attributable risk

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• Lifestyle Based Prediction (LBP)
• Most healthcare costs are driven by lifestyle
  choices
• Claims data does not reflect lifestyle
• How else can we gather this information?

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• Lifestyle-Based Prediction (LBP)
• Lifestyle-Based Prediction is based on strong
  correlations that exist between lifestyle-based
  behaviors and diseases in particular,
  lifestyle-based diseases
• LBP switches the method of detection focus from
  poorly correlated medical events to highly
  correlated lifestyle behaviors

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• Challenges in Predictive Modeling
• Predictive models are only as good as the data
  that drive them
• Challenge 1 New business
• Challenge 2 High employee turnover
• Challenge 3 Data consolidation
• Challenge 4 Increase in lifestyle diseases

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• Development of Lifestyle-Based Prediction Models
• Over 700 fields of lifestyle-based data are
  appended to two data sets
• Individuals with a disease state
• Base group average representation of the group
  at large
• Clinical datasets development
• Various models are tested including linear
  regression, logistical regression, CHAID
  analysis, discriminative analysis, Bayesian
  methods, and cluster analysis

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• Ties Between Lifestyles and Diseases
• Two types of statistical principles used in LBP
• Correlation Lifestyle-based behaviors which
  will result in a higher propensity for an
  individual to have the disease
• Obesity and latent lifestyle promote diabetes
• Causality There are lifestyle-based behaviors
  that exist or change as a result of the disease
• Once diagnosed with diabetes, you become a diet
  food purchaser

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• Lifestyle-Based Prediction Example

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• Maternity Example
• Traditional maternity factors are based on
  age/sex/geographic/family enrollment
• In fact, a simple Bayesian model using number and
  ages of children can lift results by over 40
• Lifestyle-Based Prediction can dramatically
  improve accuracy by including number and ages of
  children, financial indicators, household living
  parameters, etc.

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• Early Disease Detection Study (EDDS)
• Screening Data
• Over 100,000 patient screening records per
  condition
• Abdominal Aortic Aneurysm (AA Screening)
• Carotid Artery Disease (CA Ultrasound)
• Congestive Heart Failure (Cardiac Echo)
• Diabetes (Fasting Plasma Glucose)
• Osteoporosis (Bone Densitometer)
• Peripheral Arterial Disease (Ankle Brachial
  Index)

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• Early Disease Detection Study (EDDS)
• Health Information
• Health History
• 45 Personal health history elements
• Medical histories stroke, heart attack, CAD,
  etc.
• Medical procedures improve blood flow to heart
  or legs, prior screenings, medications, etc.
• Medical symptoms chest pain, loss of speech,
  blurred vision, etc.
• 10 Family history elements
• Medical conditions
• Medical procedures

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• Early Disease Detection Study (EDDS)
• Lifestyle Information
• Lifestyle Elements
• 8 Exercise elements
• How often do you exercise
• What types of exercise
• 5 Tobacco elements
• 8 Nutritional elements
• Caffeine intake
• Calcium intake
• Fast food intake
• Food group intake

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• Early Disease Detection Study (EDDS) Results
• Predictive coefficients for the 21
  lifestyle-based elements were relatively equal to
  the 55 health elements in all six cases
• Minimum Coronary Artery Disease
• Lifestyle-based elements relatively equal to the
  health history elements on stand alone basis
• Maximum Osteoporosis
• Lifestyle elements have twice the potential to
  affect the score compared to health history
  elements
• Combination of lifestyle with health elements
  increased health risk identification by over 45
  (as defined by R-squared)

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• Currently in Place
• Applications and enrollment forms
• Individuals and groups
• Family information
• Age, sex and age differences in family members
• Employment
• Job description
• Height/weight
• Commute time
• Geography

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• HRAs and Other Surveys
• Excellent source for lifestyle-based data
• Several key problems
• Expensive to administer (gt10/member)
• Additional cost tied to participation incentives
• Poor participation rates
• Questionable results on the unhealthiest
  population
• Timing issues for new business/members

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• Publicly Available Consumer Data
• Who, What, Where Why

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• Consumer Data in the United States
• The plethora of consumer data has dramatically
  changed our way of interacting with consumers
• Consumer data measured in Disk Storage per Person
  (DSPS)
• 1985 0.02 Mbytes/yr
• 1995 26 Mbytes/yr
• 2005 3,500 Mbytes/yr

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• Consumer Data Why?
• Primarily used for marketing, customer service
  and fraud purposes
• United States Graham-Leach-Bliley Act of 1999
• Requires opt-out
• Permitted by law
• Joint marketing agreements

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• Consumer Data Where?
• Government Public Records
• Census
• Financial Services
• Surveys
• Warranties
• Loyalty Programs
• Internet Purchases
• Subscriptions

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• Consumer Data Who?
• 95 of U.S. Households
• Historically household-based
• Newest trend individual-based
• Observed
• Implied

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• Consumer Data What?
• Traditional Demographics
• Age, sex, race, etc.
• Financial
• Homeowner, credit score, mortgage/auto/credit
  card balances, etc.
• Household
• Marriage status, number andages of children,
  etc.

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• Consumer Data What?
• Physical inactiveness
• Television time, computer time, board games,
  stamp and coin collecting, etc.

• Lifestyle-Based Elements
• Physical activeness
• Running, walking, cycling, aerobics, golf,
  tennis, etc.

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• Consumer Data What?
• Lifestyle-Based Elements
• Food purchases
• Fast food, diet food, gourmet, vegetarian, etc.
• Wine and other alcohol
• Self improvement
• Health fitness, dieting/weight loss, etc.
• Mental wellness, personal improvement, etc.

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• Consumer Data What?
• Lifestyle-Based Elements
• Tobacco
• Occupation
• Travel
• Motor vehicle type
• Recreational vehicles
• Other

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• The Expense of Consumer Data
• Medical Data Costs
• MIB, Rx, historical medical, etc. start at about
  10.00 per individual and go up
• Consumer Data Costs
• Rapidly decreasing in price due to fierce
  competition
• 5 years ago 100 data elements cost 2.00/head
• Today over 500 data elements cost 0.25/head
• The data needed for medical modeling costs about
  0.10/head or less

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• Practical Applications
• Individual
• Small group (2-50 employees)
• Medium group (51-500 employees)
• Large group (gt500 employees)

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• Practical Applications Tele-underwriting
• Determiner of At Risk population
• Who to call
• Identifier of Risk Conditions
• What questions to ask

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• Practical Application Preferred Risk
• Determination of Jet Issue Application
• Clean application plus healthy score
• Determination of Preferred Status
• Current techniques rely on clean application plus
  what?
• Lifestyle indicators provide the best what

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• Massive Consumer Database
• Over 55 million records in the US
• Every US adult over the age of 50
• Over 500 fields of lifestyle-based data
• Updated monthly
• Scored for marketing and health risk status
  monthly
• Looking at real-time hosted applications

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• Cancer Policy Example
• Model Objective
• Develop Models to Identify the Most Risky Cancer
  Policies in Terms of Claims and Track the Quality
  of Portfolio
• Rank Customers by Their Likelihood to Have Claims
  in the Next 2.5 Years
• Used in Conjunction With the Underwriting Rules
  to Validate and Improve Underwriting Process

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• Risk Model Logistic Regression
• The risk model was based on the comparison of key
  customer demographics and lifestyle
  characteristics of policyholders or applicants
  who had claims in the performance window against
  the people who do not have claims.
• The rank and plot distribution of the claims vs.
  non-claims are compared for each demographic
  attribute.
• The attributes which showed significantly
  different distributions or trends were selected
  for the Logistic regression analysis.

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• The Key Drivers of the Application Risk Model

• ISSUEAGE Customer Age at the Time of
  Application
• CHILD Presents of Children (Yes/no)
• MARRITAL Marital Status
• VEHREG Dominant Vehicle Life Style
• KID610 Have Kids Between 6 to 10 Year Old
• VEHSUV Dominant Vehicle Life Style
• ADUL35 Adult Age Under 35 in Household
• ADUL65P Adult Age Over 65 in Household
• RATIO1 Weight/height for the First Individual
• RATIO2 Weight/height for the Second Individual

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• 5 of the Customers Ranked by Scores
• include 13 of Claims

Conclusions the Lorenz Curve Shows the
Application Risk Model Rank Orders Claim Risk
Well.
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• Model Summary
• By working at the top 20 of the policies, we
  have potential to cut 43 of claims, which
  represents 45 of dollar losses. The hit rate
  (number of good policies sacrificed per bad
  policy stopped) is 14 (in the 2.5 year analysis
  window), model lifts renders 117 gains in
  targeting.

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• Profit Impact Scenario

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• Statistical Results
• Compared Traditional Underwriting and LBA Scores
  to Actual Claims Results
• LBA Beat Traditional Underwriting in All
  Statistical Measures
• Adjusted R-squared
• Bias
• MSE
• MAD
• AAD

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Operational Overview - Individual
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• Conclusion
• Recognize much of medical costs cannot be
  predicted by traditional methods
• Look for nontraditional data sources
• The real value of consumer data in the healthcare
  industry lies in its ability to predict
  lifestyle-based diseases.
• Whether used as an identifier for health risks or
  as an early predictor of a disease state, we see
  the use of Lifestyle-Based Analytics accelerating
  rapidly within the healthcare and in particular
  disease management industries.

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Questions?
Jonathan Shreve, FSA, MAAA Milliman Jon.Shreve_at_Mil
liman.com 001 303-299-9400