Personal Auto Pricing Different Since Great Recession

AR_July-Aug_2016-coverMany changes have taken place since the Great Recession, forever altering the personal auto pricing cycle. My latest Actuarial Review article, which is already attracting positive feedback, takes an in-depth look into what has affected personal auto insurance premiums since 2008.

The article, called, “The New Cycle of Pricing Personal Auto” covers several pertinent factors including:

  • The relationship between frequency and employment.
  • The curious sudden accident uptick in frequency by miles driven in the 4th quarter of 2014.
  • The gradual increase in costs per claim (severity).
  • A marked increase in driver distractions not just from cell phones but infotainment systems.
  • A growth of driving while under the influence of marijuana and accident increase in states where use is legal.
  • Auto manufacturers’ safety features reducing the frequency and severity of accidents.
  • Big data and predictive modeling transitioning from a unique pricing strategy to a common insurance business practice.
  • Low interest rates.

I am unaware of any other article that comprehensively looks into the auto insurance pricing cycle since the Great Recession. I would also like to thank James Lynch from the Insurance Information Institute for his assistance. I hope you enjoy it!

What do you think has most affected the auto insurance pricing cycle?


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For Actuaries and Underwriters, Times Are a-Changin’

AR_Jan-Feb_2016 CoverThe days of actuaries and underwriters applying their crafts through separate roles and responsibilities are on the way out, as my recent Actuarial Review article, Pricing Adjustment, explains.

To be successful in the future, actuaries need to spend more time learning to appreciate the demands underwriters face. Underwriters also need to embrace predictive modeling to appreciate its potential for pricing and marketing, experts say. Surveys show too that insurers are frustrated when their actuaries and underwriters hold to their traditional roles and work against each other.

Embracing a new approach is always easier said than done. It’s only human nature to resist change. Companies like Liberty Mutual, however, are learning that having actuaries and underwriters work more closely together boosts return on investment

Liberty’s national insurance specialty section integrates underwriters and actuaries into functional teams. The results so far have been positive, placing the insurer in a better position to address underwriting challenges while encouraging communication and understanding.

Underwriting is not the only area where actuaries should become more familiar. Past articles I have written also explain how actuaries and statisticians can complement each other and why actuaries and information technology professionals need each other.

The bottom line is the actuarial role is a-changin’. Successful actuaries will embrace new ways to work with other professions to deliver better results.

Happy reading!

Brokers Must Use Their Data for Future Success

Digital UTo attract and retain customers, insurance brokers need to take advantage of data already buried in their electronic and paper files.

My Leader’s Edge article, In the Zone: Keep Your Head in the Game Retaining Clients By Using the Competitive Data Hurtling Right at You, explains why 20th century business practices and assumptions no longer work in a world of Internet purchasing and big data.

Business insurance buyers are already shopping around online for coverage that once depended solely on business relationships. Insurance companies are already using their data to target and sell products to specific customer profiles.

And since insurers also add to their data with outside data to develop models, brokers must follow suit to retain clients and expand on the insurance coverage they are already selling.

I hope you enjoy the article. Feel free to comment below as you wish.

P.S. If you want to learn more about data and analytics, you can see past blogs on the topic under the predictive modeling tag.

Discovering the Power of F#

F# has the potential to be a game changer in the U.S. insurance industry, according to my article, A Sharp New Analytical Tool, which was just published in Contingencies’ software supplement.

If you have never heard of F#, you are not alone. It was developed in England for mathematical and analytic computations. The Microsoft programming language enjoys a larger following in Europe, where banks and insurers are already using it.

Our nation’s banking industry is already enjoying the benefits of F#, and insurance companies are likely to follow.

Grange Insurance’s experience offers a window into the industry’s future. Insurance agents are able to immediately offer quotes while the insurer can offer new products, pricing structures and experiment with predictive modeling.

The Columbus, Ohio-based property/casualty insurer got hip to F# in 2008 while it was still part of Microsoft Research. The programming language is a step beyond the better-known C#, provides faster and more accurate results than SAS and offers several advantages over the much-beloved R language.

But that is just the beginning. To learn more about F# and its potential, please read my article by clicking here.

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Who Should Be Doing Insurance Predictive Modeling? Actuaries or Statisticians?

Who Should Be Doing Insurance Predictive Modeling? Actuaries or statisticians?

Who Should Be Doing Insurance Predictive Modeling? Actuaries or statisticians?

After writing several articles on insurance predictive modeling, I noticed that there were as many statisticians doing predictive modeling as actuaries. Since more statisticians are being hired in the insurance industry, this could pose a threat to insurance actuaries. Not only that, how could a carrier, self-insured employer, third party administrator, etc., know which profession is best qualified to develop the models? What does each profession bring to the table?

You’ll find the answers to these and other questions in my article, “Professional Jealousy,” which is the cover story for Contingencies’s Job Seeker supplement. It is an honor that the American Academy of Actuaries allowed me to handle this sensitive subject.

Also, please check out my Contingencies magazine article, “The Next Great Thing in Predictive Modeling” which is probably the first comprehensive coverage of the next logical step: Integrative Predictive Modeling. For further reading, please check out my compendium on insurance predictive modeling, by clicking on the “predictive modeling” section at the bottom of this blog post! Enjoy!

Predictive Modeling Integration Is Coming

Screen Shot 2013-03-01 at 10.39.52 AMThe future of predictive modeling will not be limited to workers’ compensation.

My recently-released article, “The Next Great Thing in Predictive Modeling,” explains how predictive models will be integrated across insurance lines and traditionally siloed insurance. To describe this, I coined the phrase, “Integrative Predictive Modeling.”

To the employer, this means that the day will come when you can buy coverage for all insurances and enjoy benefits beyond the traditional multi-line discount. Through “enterprise predictive modeling,” employers will be offered integrated packages of coverage for workers’ compensation, commercial auto, general liability and even professional liability.

While at the earliest stages, insurers will be more responsive to reflecting premium based on current claims experience and not just past experience. Therefore, employers who improve their workers’ compensation and other programs will not have to wait the two-to-three year lag time to enjoy better premiums.

Some actuaries believe it will make the traditional experience modification factor unnecessary. As I covered in a different article, Beecher Carlson, the insurance brokerage firm, is offering a total cost of risk tool that more quickly responds to claims experience in the underwriting process. (To see the article, click here.


To the employer, this means that the day will come
when you can buy coverage for all insurances and enjoy benefits
 beyond the traditional multi-line discount.

Call me a visionary or deluded dreamer, but I believe predictive modeling will be a key connector needed to combine workers’ compensation coverage with health care, non-occupational disability coverages and other programs related to a workforce’s total health and productivity. This is also known as absence management and benefit integration benefits. (See my blog, Integrated Predictive Modeling How Long Must We Wait?)

I am psyched about the potential that will come from integrated predictive modeling and I am honored that the American Academy of Actuaries publishing my piece in its Contingencies magazine.

Finally, if you are interested on who should be doing predictive modeling, actuaries or statisticians, you are welcome to read my other recently-released article, “Professional Jealousy,” which is the lead article for Contingencies’ supplement, Actuarial Job Seeker.)

(This is part VI of my series on What Employers Should Know About Workers’ Compensation Predictive Modeling. I am quite ready to write about something else! 🙂)

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To see my compendium of predictive modeling articles and posts, please click the “predictive modeling” section at the bottom of this blog.

Towers Watson Survey Reveals Predictive Modeling Progression

UntitledAfter Towers Watson published its annual predictive modeling survey results a couple weeks ago, I assumed it was well covered.

My assumption was wrong. I realized that the “coverage” was mostly cutting and pasting the firm’s news release. Being familiar with the past studies, my goal here is to give this important survey the deeper coverage it deserves. The study, called, “2012 Predictive Modeling Benchmarking Survey: Advances in Implementation,” can be found here.

Previous studies revealed the benefits of predictive modeling and its use by insurance line. The new study places personal lines and commercial lines into their own buckets. My focus is on commercial lines.

Being a certifiable comp nerd, I had to know how it is progressing in workers’ compensation. To find out, I contacted Brian Stoll, one of the study’s authors, who is a source I have relied on for my predictive modeling articles. He was kind enough to supply the information shown in the chart above.


Ninety percent of the property/casualty insurers surveyed said
bottom-line performance improvements were the motivation
for applying predictive modeling.

The graph above shows that insurer use and/or interest in using predictive modeling for workers’ compensation continues. Keep in mind that the study’s mix and amount of responders changes for each study. This year’s results include smaller insurers who are likely not yet in a position to apply predictive modeling.

Ninety percent of the property/casualty insurers surveyed said that bottom-line performance improvements were the motivation for applying predictive modeling.

This confirms the assertions I make in my blog, Predictive Modeling Is Here to Stay, which was published one day before Towers Watson’s results were released.

Most carriers report bottom line profitability from predictive modeling.  Commercial lines carriers have seen positive effects in: rate accuracy (83 percent), loss ratio improvement (72 percent) and profitability (61 percent.)

Bottom line positive impacts for commercial lines carriers are: renewal retention (39 percent), market share improvement (33 percent), and expansion of underwriting appetite (22 percent). The report says that given trends, standard commercial lines will likely achieve benefit levels even more over the next few years. I agree. Commercial lines insurance has been following the lead of personal lines all along, so as efforts mature, so will results.

Another important point: midsize and larger employers report more favorable bottom line results, particularly in loss ratio improvement and profitability. “This is likely a result of first-move advantage,” the study says.

Other significant tidbits are:

  • Commercial lines carriers focus more on loss ratios than the frequency/severity models used by personal lines.
  • Predictive modeling continues to have barriers including access and cleanliness of data, IT limitations and “people/cultural issues.”
  • It appears that carriers of all the p/c lines surveyed have not yet taken advantage of reducing manual renewal costs or focused their manual underwriting activities on higher-premium or more complex accounts.

The study was based on 63 insurance executives from the U.S. and Canada. Responding companies represent a significant share of the U.S. P&C insurance market for both commercial lines insurers (21 percent) and personal lines carriers (17 percent).

(Note: This is part V of my blog series on What Employers Should Know About Workers’ Compensation Predictive Modeling.)

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Predictive modeling will more accurately reflect an employer’s specific experience. Click Here

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Workers’ Comp Predictive Modeling and Self-Insured Employers

SaveMoney3-1Predictive modeling isn’t just for insurers. Self-insured employers – or those with captives — can also better anticipate risk and save money with predictive modeling.

Insurers generally started using predictive modeling for the underwriting process. Claims predictive modeling is at the early momentum stages for insurers and a great place to start for self-insured employers.

Effective models are always about the amount of data and its quality. Self-insured and self-administered employers should be the easiest candidates to model. (To see an example, please click here and scroll to the sidebar.) Since insurance companies augment data to build their models, self-insured employers should consider doing the same.

Self-insured employers that do not self-administer will need to access their data from their third party administrators (TPAs). Some TPAs, like progressive self-insured employers, are applying predictive modeling to the claims process. Employers can also install claim processing systems that are incorporating predictive models. I would imagine that self-insuring groups have the same advantage of similar data just as insurers that specialize in specific industries.

Specific data to use depends on the model’s extent and purpose.

Claims predictive modeling covers several facets. Models can be crafted to indicate possible factors for fraud, determine how well claims are being handled, the effectiveness of medical case management or even to identify the most effective doctors.

Specific data to use depends on the model’s extent and purpose. Determining which data to model starts with using known risk factors that can affect overall claim costs. Many of these factors employers should be tracking anyway. We know, for example, that filing claims immediately improves the chances of positive outcomes. So including the injury, filing and treatment dates is important.

Including claim examiner response times also provide insight into how much the process itself impedes progress. Other factors to include incident location; doctor(s); diagnosis; employee’s length of employment; overall workforce employment length; employee age; educational level; and, co-morbidities. Since the employee-supervisor relationship can reveal a lot about a claim’s destiny, consider including it too.

Some factors can also be based on research. WCRI’s study on attorney involvement revealed that injured workers hired attorneys based on the perception they were not getting paid when really, the claim had not been accepted yet. This should inspire keeping track of the not just if an attorney is involved but the why and when it happened. Safety and workplace incident prevention also benefit from predictive modeling as well.

(Note: This is part IV of my blog series on What Employers Should Know About Workers’ Compensation Predictive Modeling.)



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Predictive Modeling and Your Insurance Agent or Broker

Predictive modeling could change the relationship you have with your agent or broker because it changes the complexion of riskiness. This is not only true for workers’ compensation, but commercial and executive coverage as well.

Currently, agents and brokers respond to insurer underwriting changes, which can happen without warning to them. Insurers are trying to do a better job communicating to their agents and brokers about new underwriting parameters resulting from predictive models.

 If insurers choose to write coverage differently for certain risks or industries, your agent or broker might have to scramble to find a new insurer. As pointed out in a previous blog, predictive models are more sensitive than the traditional experience modifier. Therefore, you could be surprised by the delicacy of insurers’ predictive models compared to the traditional experience modifier.

You might even get new and better service. Agents are increasingly becoming “strategic partners” with their clients. To improve customer retention, these agents try to help clients improve their risk profile. This is already going on in the health insurance arena where agents and brokers are offering wellness and disease management programs to improve the health of their clients’ employees.

You might even get new and better service.

Insurers have also developed predictive models to determine which agencies and brokerages more likely to bring in business. So if you like your insurer, you may end up having to use a different agent or broker.

Theoretically, agents and brokers can use predictive modeling to help their customers make better buying choices. Some very progressive agents are initiating their own efforts to identify the cost of an employer’s workers’ compensation risk through predictive modeling. They are recommending initiatives to improve risk and the pre-underwriting phase of buying insurance.

However, most do not have the resources to hire actuaries and statisticians to build models or locate appropriate data sets. And, too many agents and brokers are having difficulty adapting to computer automation. Expect them to ask more detailed questions about your company. This additional information is for the predictive models for underwriting and perhaps premium auditing applications.

Off-the-shelf workers’ compensation predictive modeling products are not yet available to agents and brokers. Until they are, you and your agent may end up at the mercy of predictive models insurers are using.

As I covered last week, predictive modeling is here to stay. We are yet to realize all of its ramifications.

Finally, in comments posted by readers, I asked how soon predictive modeling would affect them. My broad assumption is the larger the insurer you buy coverage from, the sooner and more likely you will be affected by predictive modeling. This is simply because larger insurers have more resources to devote to predictive modeling. But small-to-medium insurers are catching up because they must compete. (For more information, check out my Leader’s Edge article, Modeling the Future, which covers how predictive modeling will affect agents in much greater detail.)

Next Week’s Blog: Predictive Modeling: Opportunities for Self-Insured Employers

This is part III of my series,What Employers Need to Know About Workers’ Compensation Predictive Modeling. To read part I, click here. Part II, click here.

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