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 Walkerkwalker@pinnacleactuaries.com

630-457-1298

mchen@pinnacleactuaries.com

515-661-5469

Mike Chen

gwang@pinnacleactuaries.com

309-807-2331

Gary Wang