1. Hearing on House Bill 80: Impacts on Auto Insurance Markets and Economic Consequences
Delaware House Insurance Committee
April 5, 2017
Dover, DE
Robert P. Hartwig, PhD, CPCU
Clinical Associate Professor of Finance
Darla Moore School of Business University of South Carolina
Tel:   moore.sc.edu

2. Claim Costs Pressures in Delawar
HB 80 Does Nothing to Address the Root Cost of Higher Insurance Premiums in Delaware 2

3. eSlide – P6466 – The Financial Crisis and the Future of the P/C
Delaware’s Pvt. Passenger Auto Loss Ratio Is Trending Upward: DE vs. US, 2011 – 2016*
DE’s combined property and liability auto loss ratio is well above the US and is increasing more rapidly
Loss Ratio
Loss Ratios in Delaware Are Much Higher than the US Overall and Are Rising. HB 80 will do nothing to address these issues.
*2016 data are for the 4 quarters ending Sept. 30, 2016.
Source: ISO/PCI Fast Track data; Insurance Information Institute
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eSlide – P6466 – The Financial Crisis and the Future of the P/C

4. eSlide – P6466 – The Financial Crisis and the Future of the P/C
Delaware: Property Damage Liability Freq. & Severity Trends Are Adverse in Recent Years
Annual Change, 2012 through 2016*
Frequency and Severity of Property Damage Liability Claims are on the Rise. HB 80 Will Do Nothing to Impact these Issues.
*2016 data are for the 4 quarters ending Sept. 30, 2016.
Source: ISO/PCI Fast Track data; Insurance Information Institute
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eSlide – P6466 – The Financial Crisis and the Future of the P/C

5. eSlide – P6466 – The Financial Crisis and the Future of the P/C
Delaware: Personal Injury Protection Frequency & Severity Trends Are Adverse
Annual Change, 2012 through 2016*
Frequency and Severity of PIP Claims are on the Rise. HB 80 Will Do Nothing to Address This Issue.
*2016 data are for the 4 quarters ending Sept. 30, 2016.
Source: ISO/PCI Fast Track data; Insurance Information Institute
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eSlide – P6466 – The Financial Crisis and the Future of the P/C

6. Delaware: Collision Severity Trends Are Higher in 2014-2016*
Annual Change, 2012 through 2016*
DE’s Collision claim severity is up sharply over the past 2 years
The Average Cost of Collision Claims Is Rising Sharply. HB 80 Will Do Nothing to Address this Issue.
*2016 data are for the 4 quarters ending Sept. 30, 2016.
Source: ISO/PCI Fast Track data; Insurance Information Institute
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eSlide – P6466 – The Financial Crisis and the Future of the P/C

7. Delaware Highway Fatalities, 2006–2016
Highway deaths in Delaware remain close to their recent peak
120 Deaths =
+21.2% since 2013
Highway fatalities in Delaware over the past 3 years are the highest they have been since the Great Recession
Sources: US Dept. of Transportation, National Highway and Transportation Administration’s Fatality Analysis Reporting System (FARS) at for years For 2016, Delaware Office of Highway Safety: 7

8. HB 80 Is an Anachronism
Overreliance on Factors Such as DMV Records Assures Less Accurate Underwriting 8

9. Overall Inaccuracy of State Motor Vehicle Records
Source: Insurance Research Council, Accuracy of Motor Vehicle Records (2002).

10. Average Omission Rate for Selected Convictions
An astounding proportion of convictions are missing from DMV records
Source: Insurance Research Council, Accuracy of Motor Vehicle Records (2002).

11. Credit-Based Insurance Scores Are Highly Predictive of Loss
The Evidence Proving the Correlation Between Credit and Insurance Loss Was Established Long Ago and is Statistically Irrefutable 11

12. Texas Study (2004) Affirms Importance of Credit as a Rating Factor
CONCLUSIONS
The Texas Department of Insurance found a “strong relationship between credit scores and claims experience”
Policyholders with scores in the bottom 10% were twice as likely to files claims as those in the top 10%
TX Insurance Commissioner: scoring “significantly improves pricing accuracy” when used in conjunction with other factors
Legislation to ban scoring could disrupt Auto/HO markets
IMPLICATION
Scoring does not produce a bias against any minority/ethnic or socioeconomic group because insurers do not consider this information in the underwriting process. This implies:
All identically situated individuals, irrespective of race, ethnicity or levels of income, would be charged exactly the same amount for auto or homeowners insurance under a rating plan that permits the use of credit information in personal lines underwriting.
ALSO…credit is not a predictor of race

13. AUTO: 2004 TX Study Confirms Strong Correlation Between Credit Score & Loss Performance
The average loss per vehicle for people with the worst credit scores is double that of people with the best scores for this group.
Source: Use of Credit Information by Insurers in Texas, Texas Department of Insurance, December 30, 2004.

14. AUTO: TX Study Shows People With Good Credit Involved in Far Fewer Accidents
Drivers with the best credit are involved in about 40% fewer accidents than those with the worst credit for this group
Source: Use of Credit Information by Insurers in Texas, Texas Department of Insurance, December 30, 2004.

15. Texas Auto: Relative Loss Ratio (by Credit Score Decile, Total Market)*
Interpretation:
Those with poorest credit scores generated losses more than double that of those with the best scores
Extremely strong statistical evidence linking credit score with loss/claim outcomes:
Credit score & likelihood of positive claim (p<.0001)
Size of loss related to credit score (p<.0001)
Correlation between relative loss ratio and credit score (r = .95)
*Each decile contains approximately 15,300 policies.
Includes standard and non-standard policyholders.
1st Decile = Lowest Credit Scores
10th Decile = Highest Credit Scores.
Source: University of Texas, Bureau of Business Research, March 2003.

16. Texas 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
1st Decile = Lowest Credit Scores
10th Decile = Highest Credit Scores.
Source: University of Texas, Bureau of Business Research, March 2003.

17. Casualty Actuarial Society Credit Study
Personal Automobile Loss Ratio by Credit Category
Category
Earned Premium
Incurred Loss
Loss Ratio
Loss Ratio Relativity A
$74,279
$75,333
101.4%
133 B
158,922
124,723
78.5%
103 C
69,043
47,681
69.1% 91 D
91,746
52,688
57.4% 75
Total
$393,990
$300,425
76.3%
Category A – Unacceptable Credit Rating
Category B – No established credit history (or does not meet the definition of A, C or D)
Category C – Good Credit Rating
Category D – Excellent Credit Rating
Conning Insurance Research and Publications. 2001
Insurance Scoring in Personal Automobile Insurance: Breaking the Silence
Study also has a chart showing Loss Ratio by Credit Category and Driver Age.
Category A – Unacceptable Credit
Records with the existence of any of the following credit charateristics:
Derogatory public Records with liability amounts greater than $0.
Collection Record
Amount Past due (delinquent) of $500 or more
Category B – No established credit history
People with no Credit history
People who do not fit definitions for A, C, or D
Category C – Good Credit
No derogatory public records or collection records
No amount past due
No negative trade lines
Leverage ratio on revolving accounts less than 60%
Ago of oldest trade line at least 7 years
Category D – Excellent Credit
Same as Category C, with the following additions:
Number of non-promotional inquiries is less than 4
Age of oldest trade line 10+ years
Source: Casualty Actuarial Society

18. EPIC Study (June 2003 NAIC Meeting)
Analyzed random sample of claim records totaling 2.7 million earned car years from all 50 states for period from 7/1/00 through 6/30/01
4 MAJOR FINDINGS:
Insurance scores were found to be correlated with the propensity of loss (primarily due to frequency)
Insurance scores significantly increase accuracy of the risk assessment process, even after fully accounting for interrelationships with other variables.
Insurance scores are among the 3 most important risk factors for each of the 6 coverage types studied
Study results apply generally to all states and regions

19. 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

20. Importance of Rating Factors by Coverage Type
BI Liability
Age/Gender
Ins. Score
Geography
PD Liability
PIP
Yrs. Insured
Med Pay
Limit
Comprehensive
Model Year
Collision
Source: The Relationship of Credit-Based Insurance Scores to Private Passenger Automobile Insurance Loss Propensity Michael Miller, FCAS and Richard Smith, FCAS (EPIC Actuaries), June 2003 (Presented at June 2003 NAIC meeting).

21. Age is an Important Factor in Auto Insurance Losses
The Importance of Age and an Underwriting Factor Will Increase as the Number of Older Drivers Continues to Grow 21

22. Motor Vehicle Deaths per 100,000 Persons by Age, 2015
Motor vehicle fatality rates for older drivers are similar to and even exceed those of teenagers and youthful drivers
Deaths per 100,000 Persons
Sources: Insurance Institute for Highway Safety from Insurance Fact Book, Insurance information Institute: . Previous year data available at:
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23. Change in Number of Fatalities by Category: Drivers 65+ Up Sharply
Fatalities involving drivers 65+ in age experienced a sharp and disproportionately large increase in 2015, 8.8% in 2015 compared to a 7.2% overall increase
Sources: US DOT/NHTSA, Traffic Safety Facts, August 2016:
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24. Use of Handheld Devices Is Increasing Among Older Drivers, 2006-2015
Use of handheld devices by older drivers is increasing, suggesting that distracted driving is likely to become a growing factor in crashes involving seniors
Sources: US DOT/NHTSA, from Insurance Information Institute 2017 Fact Book, p. 181:
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25. Thank you for your time and your attention!
Twitter: twitter.com/bob_hartwig
For a copy of this presentation, me at
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