predictive modeling: rules of thumb for communicators · 2014-06-13 · score 1st 2nd 3rd 4th 5th...
TRANSCRIPT
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Predictive Modeling:Rules of Thumb for
CommunicatorsPredictive Modeling Seminar
Insurance Marketing Communications AssociationChicago, IL
September 18, 2007
Download at http://www.iii.org/media/presentations/predictivemodeling/
Robert P. Hartwig, Ph.D., CPCU, PresidentInsurance Information Institute ♦ 110 William Street ♦ New York, NY 10038
Tel: (212) 346-5520 ♦ Fax: (212) 732-1916 ♦ [email protected] ♦ www.iii.org
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PREDICTIVE MODELING:
The Basics
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Predictive Modeling: Communications Challenges
• Predictive Modeling Can Be ComplexActuaries/Economists use a variety of statistical techniquesUnderstanding how they work requires formal statistical trainingUnderwriters apply them, usually as part of an already sophisticated and automated underwriting process
• Use of Some Predictive Factors/Models May Not be Intuitive• Usage Often Not Explained or Even Revealed to Communicators• Benefits Not Well Articulated to Communicators or Customers• Failure to Recognize & Enlist Agents as Communicators• Communications Obstacles in the Regulatory Context
Regulators may have difficulty understandingTendency is to react negativelyMay seize on issue for political gain
• Models Maximize for Statistical AccuracySome May Feel Models Are Too ImpersonalInvasion of Privacy Concerns?
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Predictive Modeling:What is It?
• What is Predictive Modeling?While people (even within the insurance industry) tend to view it as new, it is in fact quite old—as old as insurance itself.DEFINITION: Predictive modeling is a process used to create a statistical model of future behavior. In insurance, predictive models are primarily concerned with forecasting probabilities, trends and relativities.*A predictive model is made up of a number of predictors, variable factors that are likely to influence future behavior or results.In auto insurance, for example, a customer's gender, driving experience, type of vehicle, driving record, miles driven, etc., help predict the likelihood and cost of future claims. To create a predictive model, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. The models may employ a simple or extremely complex and employ a wide variety of statistical techniques.
• Use of Some Predictive Factors/Models May Not be Intuitive*Adapted and modified by the Insurance Information Institute from www.searchdatamanagement.comaccessed Sept. 16, 2007.
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Predictive Modeling: Why Do We Hear So Much About it Today?
• Insurers rewrote their entire auto and homeowners book of business beginning in the later 1990s/early 2000s in response tohuge losses in both of these key lines (which together account for nearly 50% of industry premiums)
• This re-underwriting process was effectively a re-evaluation of risk presented by each policyholder and the adequacy of the premium paid by the policyholder to transfer that risk.
• In most cases the premium was inadequate and premiums rose• Re-underwriting process included the use of sophisticated new
models designed to better match price with risk• By definition, these models included more and better rating
factors as well as new statistical methodologies for gauging interactions between these factors.
• Policyholders and regulators incorrectly associated new factors in the models as being solely responsible for the increase
• Credit-based “Insurance Scores” are the best known example
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101.7101.3101.3101.099.5
101.1
103.5
109.5107.9
104.2
98.4
94.395.1 95.5
90
95
100
105
110
93 94 95 96 97 98 99 00 01 02 03 04 05 06
Private Passenger Auto (PPA) Combined Ratio
Average Combined Ratio for 1993 to 2005:
101.0
Sources: A.M. Best; III
PPA is the profit juggernaut of the p/c
insurance industry today
Auto insurers have shown significant improvement in
PPA after re-underwriting entire book of business in
early 2000s
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Predictive Modeling: Why Now?
• Predictive modeling is not new—big issue in most industries• Some form of it has been around since the earliest days of
insurance—used in personal and commercial lines• In recent years the cost of data storage and acquisition have
declined as has the cost of computing power• More data is available to insurers today at lower cost• Powerful computers make analysis (mining) of the this data
easier, faster and more fruitful• Public and regulators have pushed for more individualized rates
(and less reliance on factors like territory)• Insurers responded by accelerating trend toward individual risk
rating smaller pools of increasingly homogeneous individuals• Consequently, rating systems becoming fairer & more accurate• Implies that subsidies are being removed from system• Recipients of subsidies don’t like their removal nor do regulators
who view insurance as an extension of the social welfare system
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Insurance Scores:The Perfect Example of a
Communications Breakdown• Insurers began to implement use of credit-based insurance score
in the early/mid-1990s, but not on a large scale until late 1990s very early 2000s.
• Insurers had found that scores were among the most accurate of all rating factors for predicting future loss.
• Roll-out and use of credit was not communicated to most key personnel who come in contact with customers, regulators or media
• Why credit works was not intuitive for most people (e.g., what does credit information have to do with my driving ability?)
• Agents dislike having to explain why premiums rose due to creditfactors
• Special cases warranted special treatment abounded: No credit, life-changing events, identity theft
• Consumer protections formalized only later (e.g., NCOIL)• Race issue became (and remains) big (but is red herring)
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PREDICTIVE MODELING:JUST PART OF THE
RATEMAKING & UNDERWRITING PROCESS
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Predictive Data Can Be Historical, Class or Individual Specific
• Historical Information: Used to identify trends in dataActuaries use a variety of statistical techniques; get base rate
• Class RatingData are adjusted for geographic, industry-specific factors or other factors statistically correlated with risk of future lossE.g. Urban zip codes = greater accident frequencyE.g. Occupation in workers comp
• Individual Risk RatingPolicyholder-specific risk factors are taken into account
E.g., Model of car; wood frame vs. masonry home; office vs. construction workerCredit profile“Black box” data;FUTURE: GPS Tracking (on voluntary basis)
• Experience RatingAdjustments made to premium based on policyholder’s past claim filing activity
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UNDERWRITING:
Key to Accurate Risk Assessments & Rates
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What is Underwriting?• Underwriting
Process by which insurer determines whether policy should be issued and on what terms
• Complex ProcessMany market and individual factors consideredAll relate to riskiness/likelihood of loss
• Insurers All Use Underwriting GuidelinesHelps keep insurers focused, disciplined, profitable, solventE.g., no writing risks within 5 miles of coast, no high-rise construction risks, no limits above $1 million, no sportscars
• Underwriting ToolsObjective is to improve accuracy of loss forecastsCreates a more fair, equitable rating system for allPremium is more closely associated with risk
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RATING FACTORS
Helping to Match Premium Charged to
Risk Assumed
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Categories of Typical Auto Insurance Rating Factors/Criteria
• Vehicle Type Factors• Use of Vehicle Factors• Location (Territorial) Factors• Driving History• Prior Insurance• Personal Factors• Other
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Typical Auto InsuranceRating Criteria
• Vehicle Type FactorsNumber of vehicles to be insured on policyNumber of operators in householdMake, model & body style of each vehicleAge of vehicle (model year)Safety features (e.g., airbags, anti-lock brakes)Anti-theft devices
• Use of Vehicle FactorsDistance driven annuallyCommuting distanceNumber of days per week used to commuteWho drives vehicle the most?Years of driving experience (youthful operator?)Use of vehicle for business purposes
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Typical Auto InsuranceRating Criteria
• Location (Territorial) FactorsLocation where vehicle is keptGarage or street parking
• Driving HistoryAccidentsMoving violationsConvictions (e.g., DUIs)Personal claims history
• Prior Insurance FactorsCurrently insured?Number of years with current insurer?Current Bodily Injury limits
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Typical Auto InsuranceRating Criteria
• Driving HistoryAccidentsMoving violationsConvictions (e.g., DUIs)Personal claims history
• Prior Insurance FactorsCurrently insured?Number of years with current insurer?Current Bodily Injury limits
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Typical Auto InsuranceRating Criteria
• Personal FactorsMarital StatusGenderOccupationEducationStudent?Homeowner?
• Other FactorsInformation from credit reportsDrivers education, defensive driving course taken
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Examples of Relationships Between Underwriting Criteria
& Losses
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Example 1:
GENDER & AUTO INSURANCE
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Millions of Accidents
7.67.0
7.5
9.68.6
7.4
9.9
8.6
12.1
8.4
12.4
10.611.4
14.3
12.7
10.6
15.2
12.7
18.6
11.6
6
8
10
12
14
16
18
20
94 95 96 97 98 99 00 01 02 03
Mill
ions
of A
ccid
ents
Female Male
Sex of Drivers Involved in All Auto Crashes, 1994-2003
Source: National Safety Council; Insurance Information Institute 2005 Fact Book, p. 109.
Males are involved in 50% more
accidents on average
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Fatalities per Billion Miles Driven
1715
17 1716
12
1614
13 13
2524
27 2725
18
27
2422 22
5
10
15
20
25
30
94 95 96 97 98 99 00 01 02 03
Fata
litie
s per
Bill
ion
Mile
s Dri
ven Female Male
Fatality Rate by Sex of Drivers Involved in Auto Crashes, 1994-2003
Source: National Safety Council; Insurance Information Institute 2005 Fact Book, p. 109.
Males are involved in 61% more likely
to be killed in an auto accident
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Example 2:
DRIVER AGE
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4.8%
8.4%
17.3
% 20.8
%
20.7
%
13.3
%
8.2%
6.5%
22.1
%
18.3
%
17.8
%
15.8
%
12.5
%
7.0%
3.6%
2.9%
0%
5%
10%
15%
20%
25%
Under20
20-24 25-34 35-44 45-54 55-64 65-74 75+
Percent of Total Drivers Share of Accidents
Accidents by Age of Driver, 2003
Source: National Safety Council; Insurance Information Institute
Teens account for just 5% of drivers
but 22% of accidents! But people 35-44
represent 21% of drivers but just
16% of accidents
Teens are by far the most likely to be involved in accident than the elderly (but elderly more
likely to die in crash)
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Example 3:
INSURANCE SCORING (CREDIT)
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Importance of Rating Factors by Coverage Type
Ins. ScoreAge/GenderModel YearCollision
Ins. ScoreAge/GenderModel YearComprehensive
Age/GenderLimitIns. ScoreMed Pay
Yrs. InsuredGeographyIns. ScorePIP
GeographyIns. ScoreAge/GenderPD Liability
GeographyIns. ScoreAge/GenderBI Liability
Factor 3Factor 2Factor 1Coverage
Source: The Relationship of Credit-Based Insurance Scores to Private Passenger Automobile InsuranceLoss Propensity Michael Miller, FCAS and Richard Smith, FCAS (EPIC Actuaries), June 2003 (Presented at June 2003 NAIC meeting).
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Average Loss = $695
$668
$918
$846
$791
$707 $703$681
$631
$584 $568 $558
$500
$600
$700
$800
$900
$1,000
NoScore
1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th
Score Range
Avg
. Inc
urre
d L
oss p
er P
olic
yTexas Auto: Average Loss per Policy
(by Credit Score Decile, Total Market)
Interpretation:
Those with poorest credit scores generated incurred losses 65% higher
those with the best scores
Source: University of Texas, Bureau of Business Research, March 2003.
1st Decile = Lowest Credit Scores
10th Decile = Highest Credit Scores.
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9%
33%
18%
10%
3%0%
-7%-11%
-14% -15%-19%
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
NoHit/Thin
File
607 659 693 722 748 774 802 837 894 997
Score Range
Rel
ativ
e Pu
re P
rem
ium
Indicated Relative Pure Premium by Insurance Score (PD Liability)*
Interpretation:
Those with poorest credit scores had loss experience 33% above average while
those with the best scores had loss experience that was 19% below average
Source: EPIC Actuaries, June 2003
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Example: Credit Discount Can Save $100s per Year*
Good Driver Discount
24%
Credit-Related
Discount36%
Safety/Anti-Theft
Discount19%
Multipolicy Discount
21%
$296
$174
$196
$154
*Annualized savings based on semi-annual data from example
Source: Insurance Information Institute
•Credit discount lowered annual premium by 14.7%
•Policyholder saved nearly $300
•Credit was single largest discount
•Opponents of credit will force people to pay more for coverage
Total Annual Savings from Discounts: $820
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Example 4:
WORKER AGE
(A Workers Comp Example)
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THE AGEING WORKFORCEAge Could be Used a
Predictor of Occupational Injury and
Loss, But it is Not
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Source: US Bureau of Labor Statistics, 2004.
40.539.0
35.834.3
35.236.6
38.039.4
40.6 40.7
30
32
34
36
38
40
42
1962 1970 1975 1980 1985 1990 1995 2000 2005 2008Year
U.S. Workforce is Aging: Significant Implications for Workers Comp
Median Age of U.S. Worker
The median age of US workers as the Baby Boomer begin to retire is about 41 years. Immigration will hold this
number down and may even lower the figure.
Older and less healthy workforce
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Fatal Work Injury RatesClimb Sharply With Age
4.94.03.63.22.72.7
0.8
10.8
0
2
4
6
8
10
12
16-17 18-19 20-24 25-34 35-44 45-54 55-64 65+
Source: US Bureau of Labor Statistics, US Department of Labor; Insurance Information Institute.
Fatality rates for workers 65 and older are triple that of workers age 35-44. The workplace of the future will have to be completely redesigned to accommodate
the surge in older workers.
Fatal Work Injuries per 100,000 Workers (2006)
Age is not used as a an underwriting factor in WC—should it be?
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Example 5:
WORKER WEIGHT
(Another Example Relevant to Workers
Comp that is Not Used)
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THE OBESITY EPIDEMIC
Major Cost Driver that WC Has Yet to Address
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WC Claims and Lost Workdays by Body Mass Index (BMI)
40.9760.17
75.21
14.19
183.63
117.61
5.53 5.807.05
10.80
8.81
11.65
020406080
100120140160180200
BMI <18.5(Underweight)
18.5-24.9(HealthyWeight)
25-29.9(Overweight)
30-34.9 (ObeseClass I)
35-39.9 (ObeseClass II)
40+ (ObeseClass III)
Los
t Wor
kday
s per
100
FT
Es
0
2
4
6
8
10
12
14
Cla
ims p
er 1
00 F
TE
s
Lost Workdays Claims
Obesity is not a rating factor, but it is an
identifiable cost factor
Source: Ostbye, T., et al, “Obesity and Workers Compensation,” J. of the American Medical Association, April 23, 2007.
The most obese workers file twice as many WC claims and 13 times more lost workdays than healthy weight workers
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Medical & Indemnity WC Claims Costs by BMI
$7,1
09
$13,
338
$19,
661
$3,9
24
$5,3
96 $13,
569
$34,
293
$7,5
03
$51,
091
$23,
373
$23,
633
$59,
178
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
BMI <18.5(Underweight)
18.5-24.9(HealthyWeight)
25-29.9(Overweight)
30-34.9 (ObeseClass I)
35-39.9 (ObeseClass II)
40+ (ObeseClass II)
Medical Claims Costs Indemnity Claims Costs
Med claims costs are 6.8 times higher for the most obese
workers and indemnity costs are 11 times higher
Source: Ostbye, T., et al, “Obesity and Workers Compensation,” J. of the American Medical Association, April 23, 2007.
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Example 6:
TERRITORY
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2.11
1.47
3.00
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Bodily Injury Liability Property DamageLiability
Personal InjuryProtection
Baltimore Relativity toState Loss Cost, 2001-2003
*ISO territories 33, 35, 36 and 39.Source: ISO.
BI Liability costs in Baltimore are more than double (2.11 times) the state overall (i.e., 111% higher)
PD Liability costs in Baltimore are 47% higher
than the state overall
PIP costs in Baltimore are triple the the state overall
(200% higher)
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2.37
1.47
2.52
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Bodily Injury Liability Property DamageLiability
Personal InjuryProtection
Baltimore Relativity toState Loss Cost, 1988
*ISO territories 33, 35, 36 and 39.Source: ISO.
Costs in Baltimore were well above average back in
1988 too—still are today and will be in the future.
This is permanent feature of most major urban auto
insurance markets
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Are There Limits to What Predictive Modeling Can or Should Do?
• Predictive Modeling Increases Accuracy, Equity in Rates
Incumbent on insurers to use this information subject to limits imposed by policymakers
• Advances in Data Storage, Retrieval, Computing Will Advance the Frontier of Predictive Models
• Concern that Individual Risk Rating Will Replace Risk Pooling is Absurd
No model will ever be 100% accurateSome degree of pooling will always exist
• Societal Boundaries Will Always ExistPredictive modeling will never be used to its full potentialPrivacy/”Big Brother” concerns
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Predictive Modeling: 6 Rules of Thumb for Communicators
1. EDUCATE: Educate Yourself to Develop Understanding of How Products Work
Get to know actuaries and underwriters in your company2. PARTICIPATE: Get Communications (not just Marketing)
Involved at a Much Earlier Stage of Product Cycle3. ANTICIPATE: Potential Communications Challenges
Before Rollout4. IDENTIFY: Subject Area Experts as Technical Resources5. DISSEMINATE: Create Plan to Help Employees with
Customer, Regulator & Media Contact Understand How Product Operates
6. COORDINATE: Ensure Marketing, Government Affairs, Customer Service, Agents all Operating from Same Playbook
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Insurance Information Institute On-Line
If you would like a copy of this presentation, please give me your business card with e-mail address, or dowload at:
http://www.iii.org/media/presentations/predictivemodeling/