February 18, 2016
Michael ChenKatey Walker
Gary Wang
Auto Rating Plans for the New Year (and Beyond!)
1
About the presenters
Kathryn A. Walker, FCAS, MAAA, CPCU
• Consulting Actuary• Chicago, Illinois Office• 16 of years experience
Gary Wang, FCAS, MAAA
• Consulting Actuary• Bloomington, Illinois Office• 17 of years experience
Michael K. Chen, FCAS, MAAA
• Consulting Actuary• Des Moines, Iowa Office• 12 of years experience
2
• Personal auto insurance rating plans
• Recent changes:
– New rating concepts
– Innovative uses of common rating variables
– Predictive analytics
• Impacts from:
– Territory definitions
– Usage-based insurance
– Competitive intelligence
• And beyond!
Overview
3
1916
1906
1896
1886
What year did the first auto accident occur?
A
B
C
D
Polling Question #1
Massachusetts
Wyoming
New Hampshire
Mississippi
A
B
C
D
Which state is considered the most liberal for auto insurance laws?
Polling Question #2
4
2000’sPredictive analytics, usage-based insurance, household views
1990’sIntroduction of credit scoring
1980’sCompanies are using tiers and multiple rating programs
1950’sDriving record included in rating plan
1939Age, mileage, and use incorporated as rating variables
1927Massachusetts enacts the first compulsory insurance requirement for vehicle registration
1925Connecticut implements the first Financial Responsibility law
Rating plan origins
5
• Similar coverage and product offerings
• Similar rating variables
• Similar class segmentation
Companies featured:
• Overall rate level
• Marketing / relationships / commissions
• Underwriting
• Target niches
Focus on:
Traditional ratemaking–limited differentiation
6
Traditional
• “Me too”
Innovation
• New coverage options
• Non-traditional variables
• Refined class segmentation
• Behavioral-based elements
Modern
• Proprietary models
• Product/portfolio management
• Customer incentives
• Lifetime value
Ratemaking evolution
7
Drivers attributes
• Age
• Gender
• Martial status
• Driving record
• Driver training
• Driver status
• Driver behavior
• Occupation
• Education
Vehicle attributes
• Location
• Use
• Mileage
• Age of vehicle
• Value
• Safety devices
• Performance
• Symbols–by coverage
Policy attributes
• Multiple vehicles
• Coverage limits
• Deductible
• Prior insurance
• Homeowner
• Payment methods
• Credit score
• Driver-vehicle matrix
• Household structure
• Shopping behaviors
• Long-term value
• Discounts
• Affinity rating
Rating variables
8
Generalized Linear Models (GLMs)
• Multivariate framework
• Impact of non-intuitive variables
• Refined rating segmentation
Third party data
• Weather
• Highway Loss Data Institute
• Census data
• Quote information
Integration of customer behavior data
• Retention analysis
• Conversion analysis
Enter…predictive analytics
9
Predictive Analytics
• Part of the analytics solution
• Cannot replace human expertise with model output
Good models don’t give the “right answers”
• Answers specific questions
• Explains broader concepts
Models and rating
10
Conversion & retention analysis
Territory definitions
Loss cost predictions–frequency & severity
UBI–driver scoring
Claims triage
Agent commissions
Business Strategy
Incorporation of models
11
Within the past year
Within the past 3 years
Within the past 5 years
Within the past 10 years
More than 10 years
When was the last time your company updated the territory analysis procedure?
A
B
C
D
E
Polling Question #3
Territory Analysis
13
• Hazard data providers
– Weather
– Crime
• Census and other governmental data
– Housing density
– Crime statistics
– Population density
– Household structure
• Catastrophe model output
Geodemographic data sources
14
• Industry level data
– Membership includes all of the top 10 personal auto insurance companies
– For each calendar year, 10 latest model years data
– Key coverages available
• BI, PD, Comp, Coll, Med, PIP
Bulking up the data–HLDI
15
• Eliminate effect from all other rating variables
• Capping
• Smoothing
• Normalization
• Weighting together of various internal and external data sources
Data adjustments
16
• Key smoothing variables
– Predictive value of local data
– Identification of complement data
– How many observations are required to smooth
– How far to allow smoothing search to continue
• Many equations are available to combine local data with surrounding information
– Exposure Weighted Average
– Straight Line Declining Distance formula
– Squared Declining Distance formula
Smoothing
17
Smoothing
• Data at the basic element level lacks “credibility”
• Smoothing process allows inclusion of more localized data rather than statewide information
• Results in a rate or rate relativity for each individual zip code based upon the data within that zip code modified as necessary to include a significant number of observations
18
Smoothing considerations
• State borders and corners
• Use of smoothing across state boundaries
• Potential separate smoothing of urban and rural areas
• Distance based smoothing process or contiguous based smoothing process
19
Smoothing impact
20
• Selection of Target Optimal Clusters for use in establishing territories based on analysis of variance data
• Goal
– Risks within territory very similar to each other
• Minimize within variance
– Risks outside territory different from those within
• Maximize between variance
Optimal cluster evaluation
21
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Pe
rce
nt
of
Tota
l Va
rian
ce
Number of Clusters
Optimal cluster evaluation
Within Variance / Total Variance
22
Helpful to look at a variety of cluster sets to provide guidance when making judgmental changes, based on:
• Size of resulting territories
• Past events distorting results
• Competitive considerations
Territory definition selections
C luster T o Review
15
Proposed Terr:
Exposure
Weighted PP Exposure Zip Count
Exposure
Weighted PP Exposure Zip Count
Exposure
Weighted PP Exposure Zip Count
1 385 16396 4 385 16396 4 400 7262 2
2 353 4929 3 353 4929 3 373 9134 2
3 317 3665 3 317 3665 3 353 4929 3
4 297 9170 9 297 9170 9 317 3665 3
5 266 10391 9 278 4670 4 297 9170 9
6 229 44776 42 255 5721 5 278 4670 4
7 197 71087 49 229 44776 42 255 5721 5
8 181 63994 62 197 71087 49 229 44776 42
9 165 120410 133 181 63994 62 197 71087 49
10 150 82311 118 165 120410 133 181 63994 62
11 139 61094 58 150 82311 118 165 120410 133
12 130 54651 47 139 61094 58 150 82311 118
13 117 69135 33 130 54651 47 139 61094 58
14 103 4261 3 117 69135 33 130 54651 47
15 0 103 4261 3 117 69135 33
16 0 0 103 4261 3
1614 15
Usage-Based Insurance
24
My company markets a UBI based personal auto insurance policy
My company is developing a UBI based personal auto insurance policy
My company is piloting UBI products
My company is not interested in UBI
What statement best describes your company’s view on usage-based insurance?
Polling Question #4
A
B
C
D
25
Usage-based insuranceCurrent market penetration
Insurance Group 2014 NWP (000's)
1. State Farm Group 35,372,515
2. Berkshire Hathaway Insurance Group 20,552,102
3. Allstate Insurance Group 18,836,252
4. Progressive Insurance Group 16,374,660
5. USAA Group 9,824,529
6. Liberty Mutual Insurance Companies 8,445,483
7. Nationwide Group 7,258,632
8. Farmers Insurance Group 7,249,243
9. American Family Insurance Group 3,516,209
10. Travelers Group 3,389,868
11. Hartford Insurance Group 2,623,599
12. Erie Insurance Group 2,357,010
13. Auto Club Enterprises Insurance Group 2,348,951
14. MetLife Auto & Home Group 2,238,005
15. Mercury General Group 2,219,159
16. CSAA Insurance Group 2,142,687
Progressive Snapshot®:2.6 Billion
26
• Progressive Snapshot®
– 2.6 billion premium written
– 12 billion miles of recorded miles
– 2.5 million participants
• Progressive® Insurance has formally chosen Censio to develop the software for the insurance company's leading usage-based insurance program, Snapshot. The company will begin a pilot of the mobile app with select customers across the country in mid-September.
-Sep 2, 2015
Keeping an eye on the leader
27
• Mobile app solutions– Allstate– Progressive
• OEM systems– Ford SYNC®
• State Farm– OnStar
• National General Insurance• State Farm• Progressive• VERISK!
Moving beyond the OBD-II port devices
• Verisk Telematics Data Exchange– Verisk insurance solutions
announces GM as inaugural auto manufacturer to join telematics data exchange.
-Sep 2, 2015
– Expected rollout: summer 2016
28
Things to consider for rating:• What data will you initially use in rating? Are those data
elements collected by your device?
Mileage
Excessive acceleration
Hard braking
Speeding
Cornering
Time of day
Location
• Do you plan on updating the rating once you collect more of your own data?
Will additional data elements be used?
Are those additional elements currently collected?
Considerations for a successful UBI pilot program
29
• How can data on the UBI experience of consumers shape product development, pricing and customer service offerings?
• How important are UBI device options to the consumer?
• What are the expectations of the consumer regarding savings?
• What other services are important to consumers in a UBI program?
In the UBI of the consumer
30
• Analysis–sentiment of social media posts related to UBI programs– Text mining
– Review of individual tweets
• Data– Over 6.5 million insurance
tweets total (January, 2012 to present)
– Keyword searches for terms related to UBI programs in US, UK and Canada
– UBI information collected since October 2012
Description of analysis–social media data
• Data fields Content of the tweet
Specific tweet recipient
Sender of the tweet
Language of tweet
Where the tweet originated
Link to a picture of user
Latitude and longitude of the user
Date and time of tweet
User profile
31
23.4%
18.2%
14.1%
13.4%
18.9%
24.4%
9.8%
12.2%
8.8%
15.8%
12.3%
16.7%
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%
Privacy
Misunderstanding of Program
Rate Can Go Up
Concerned About Results
2013 Study 2014 Study 2015 Study
Non-customer negative responses–US
“Pretty sure if I tried Snapshot, Progressive
would triple my premium.”
Progressive Snapshot
'bombshell': Device can raise users'
rates
32
76%
14%
6%
4%
76%
14%
0%
2%
74%
7%
0%
13%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Savings
Excitement
Tracking
Better Driving
2013 Study 2014 Study 2015 Study
Positive customer responses–US
33
23%
16%15%
14%
10%
4%
24%
18%
20%
11%
1% 1%
16%
8%
22%
10%
2%
0%
0%
5%
10%
15%
20%
25%
30%
Driving Habits Beeping Snapshot Evaluation Annoying Premium Change Installation
2013 Study 2014 Study 2015 Study
Customer complaints–US
34
Savings–US
2%
59%
85%
96% 94%
2%
6%
0%
1%0%
96%
35%
15%
3%6%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Zero Less than 10% 10% to 19% 20% to 29% 30% and Greater
Ton
e P
erce
nta
ge f
or
Each
Dis
cou
nt
Cat
ego
ry
Reported Discount
Reported Snapshot Discount by Tone
Positive Neutral Negative
35
• Value proposition has to be compelling for customers
• Device options are not as important as ensuring that the options work
• Ultimate customer satisfaction is dependent on customer perception of driving ability relative to UBI evaluation
• While value added services may increase the chance of conversion, the most significant reason for trying and staying with UBI is savings
Key conclusions
Competitive Intelligence
37
Validate existing rates
Marketing reports
Test rating plan proposal
Define business strategy
Metrics
How is competitive quote information currently used in your company? (choose all that apply)
Polling Question #5
A
B
C
D
E
38
Competitive intelligence–evolution
Traditional
• Competitor rate filings
• Key quote analysis
• Agent feedback
And Beyond!
• Big data
• Market basket
• Demographics
• Industry and research data
39
Strategy
Underwriting
Marketing
Competitive Intelligence
Rating Plans
Product Development
Business strategy
40
Analytics Hindsight Insight Foresight
Current State–what carriers are doing today
75% 20% 5%
How do I get market data?No market data;
ask agents or rate filingsBuy manufactured data
Buy from additional outside sources
What am I doing with market data?
Internal data - conversion rates, premium, sales goals
Compare toPeers: evaluate agents, profitability, marketing effectiveness, compare product performance
Compare to Peers: Expansion plans, cut
products, change pricing or product, do strategy responding to changing
market
What problems will I solve if I have more data?
Internal data - evaluate agents, profitability,
marketing effectiveness, compare product
performance
Expansion plans, cut products, change pricing or product, do strategy responding to changing
market
What will happen If / then analysis
Sensitivity analysis
Consumer data & analytics
41
Current factors
• Insurance score
• Multi-line discount
• Multi-car discount
• Driver/vehicle matrix
• Prior insurance
• Age/gender/marital status
Potential additional elements
• Billing frequency and payment history
• Mileage (UBI and non-UBI)
• New business/disappearing discounts
• Affinity discounts
• Non-chargeable accidents and claims
• Vehicle history
• Vehicle symbol
• Use of MVR’s
Rate segmentation/competitive position assessment
42
Leveraging consumer shopping behavior
Advantages
Limitations
• Provides behavioral element• Real information• Broader market perspective• Industry variables
• Information often self-reported• Cost of information verification• Non-universal variables• Timeliness & updates
43
Competitive intelligence
1,800
1,600
1,460
1,425
1,500
800
1,000
1,200
1,400
1,600
1,800
2,000
Pre
miu
m (
$)
Average Policy Rate Level
Uncompetitive
Competitive
Too Competitive
Current
Proposed
Adj Proposed
Target
Median
44
Comparative rater data
Date/Time
State
Carrier Reference
Carrier Name
LOB
Premium
Rank
Number Selected
Number Quoted
High Premium
Low Premium
Average Premium
Median
Mode
Mean
Range
Vendor
BatchQuote Data
Policy Number Model Year
Quote Number Vehicle Make / Model
Quote Date Annual Mileage
Effective Date Vehicle Use
Agency Deductible
Status Vehicle Symbols
City Anti-Theft Devices
State Anti-Lock Brakes
Zip Number of Claims
Insurance Score Number of Violations
Marital Status Prior Insurance
Birth Date Insurance Score
Gender BI/PD Limits
Rating Tier Comp/Collision
Occupation Medical Payments Limit
Good Student Uninsured Motorist Limit
Driver Training Payment Method
County Advanced Shopper
Territory PAC Credit
Company
DetailedQuote Data
Key Rating Variables
Conversion Indicator
Competitive Position
45
Case study–competitive analysis solution
Measure of current premium –proposed premium – competitor
premium
Average premiums by variable to identify opportunities and areas of concern
46
Output example–vehicle age
-
1,000
2,000
3,000
4,000
5,000
6,000
-
0.200
0.400
0.600
0.800
1.000
1.200
1.400
1.600
1.800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 42 44 46 49 53 57 510
Num
be
r o
f Q
uo
tes
Rela
tive
Diffe
ren
ce
Newest Vehicle Age
Vertafore Market Basket Analysis Arizona Newest Vehicle Age
Number of Quotes Average Premium Median Comp X
47
Other than rate indications you only have the resources to perform one other actuarial study for 2016. What would you choose to research?
Polling Question #6
New variable development
Territory analysis
Competitive analysis
Usage-based insurance
A
B
C
D
48
Future advancements:
• Autonomous vehicles
• Safety technology
• Connected technology
• Weather & CATs
Challenges:
• Regulatory & social acceptability
• Ability to verify inputs vs cost
• Significant impacts of changes
• Change is hard –agents, customers, employees
Benefits:
• More accurate rates
• More meaningful segmentation
Conclusions
49
Join us for the March Apex
50
• We’d like your feedback and suggestions
• Please complete our survey
• For copies of this APEX presentation
• Visit the Resource Knowledge Center at Pinnacleactuaries.com
Final notes
51Commitment Beyond Numbers
Thank you for your time and attention
Katey [email protected]
630-457-1298
515-661-5469
Mike Chen
309-807-2331
Gary Wang