Why Actuaries Will Become Data Scientists

Insurance today takes many forms: life insurance, health insurance, property insurance, casualty insurance (liability insurance for negligent acts), marine insurance, and catastrophe insurance (covering perils such as earthquakes, floods, windstorms, and terrorism). You name it, you can probably insure it. Prior to the insurance innovation that led to the varieties just mentioned, a branch of management science was established that served as the root of insurance: actuarial science. Actuaries are in the business of assessing risk and uncertainty. Said a different way, they value and assess financial impact, of a variety of risks. But that is much easier said than done. A variety of inputs provide the information an actuary needs for better decision-making.

The Institute and Faculty of Actuaries (IFoA) is the only professional organization in the United Kingdom dedicated to educating, regulating, and generally advocating for actuaries worldwide. If Ben Franklin is the father of insurance, Chris Lewin, associated with IFoA, is probably the original actuarial historian. He has published regularly on the topic and is well known in the community for his significant contributions. Lewin states, “An actuary looks at historical data, and then makes appropriate adjustments (subjective, of course).” One of the primary skills of an actuary, therefore, is to make estimates based on the best information available. Even if an actuary uses data to develop an informed judgment, that type of estimate does not seem sufficient in today’s era of big data. There is some- thing about modern-day insurance that has led the industry to believe that informed judgment is good enough. As the quantity and quality of data improves, it will be possible to calculate increasingly accurate estimates based directly on information, negating the need for human judgment and associated biases.

***

The data era has already begun to spark a new wave of innovation in insurance. Rich access to a variety of data assets, coupled with the ability to analyze and act, enables processes that were not previously possible. This will usher in the era of dynamic risk management and improved approaches for modeling catastrophe risk in the insurance industry. In the case of automobile insurance, the industry commonly refers to this type of insurance as Usage Based Insurance. There are two types of policies under this type of insurance — Pay-As-You-Drive (PAYD) and Pay How You Drive (PHYD). However, dynamic risk management can apply well beyond the scope of driving and automobile insurance.

Dynamic risk management is an accelerated form of actuarial science. Recall that actuarial science is about collecting all pertinent data, using models and expertise to factor risk, and then making a decision. Dynamic risk manage- ment entails real-time decision-making based on a stream of data. Let’s explore the two models with an example of car insurance for a 22-year-old female:

Actuarial insurance: Collect all the data available for the 22 year old — her driving history, vehicle type, location, criminal history, etc. Merge that data with demographic data for her age, gender, location, and work status. Leverage methods like probability, mortality, and compound interest to estimate benefits and obligations. Then, offer a policy to the woman, based on these factors.

Dynamic risk management: Install a sensor in her car and tell her to go about her normal life. Collect mileage, time of day she drives, how far she drives, acceleration/deceleration, and the locations that she drives to. When she is driving, monitor the motion of the vehicle. Said another way, this is an on-board monitor, constantly pricing her insurance policy based on her personal driving behavior. If she drives well, her next premium may be lower. The policy is tailor-made for her and is based on actual data, as opposed to estimates.

There is now increased momentum for dynamic risk-management. In March 2011, the European Court of Justice stated that “taking the gender of the insured individual into account as a risk factor in insurance contracts constitutes discrimination.” Since December 2012, insurers operating in Europe are no longer able to charge different premiums on the basis of an insured person’s gender. There are good reasons why insurers might want to use gender as a means of quantifying risk. Men under the age of 30 are almost twice as likely to be involved in a car accident as their female counterparts. Insurers also have empirical evidence to show that the claims they receive for young men are over three times as large as those for women.

Arguments around gender equality have rightly determined that it is unfair to blindly discriminate against young men. Furthermore this debate has highlighted the need for more appropriate metrics for forecasting risk rather than the blunt use of gender. This gap in the market calls for better models and dynamic risk management based on the actual driving ability of the individual. Unfortunately, in the meantime, we are all paying the price as car insurers have increased their premiums across the board.

Currently, many of the large insurance carriers offer some version of dynamic risk management, or pay-as-you-drive insurance for automobiles: Progressive, Allstate, State Farm, Travelers, Esurance, the Hartford, Safeco, and GMAC, to name a few. Most of the insurers market that premiums will cost 20 to 50 percent less for consumers who adopt this approach. The National Association of Insurance Commissioners estimates that 20 percent of insurance plans will have a dynamic approach, by 2018. For the moment, dynamic insurance for automobiles is less than one percent of the market.

Dynamic risk management can apply to any data-centric insurance process, whether a company is leveraging telematics or data points about a consumer in a lending scenario. In the Big Data era, dynamic risk management will become routine.

Insurers could make themselves more popular by recognizing that dynamic risk management could become a means for encouraging behavior change. Rather than offering non-negotiable premiums based on coarse models, the use of big data to assess individual risk would urge those customers to behave more responsibly. In this way, insurance could provide a price signal to nudge customers toward a lower-risk lifestyle. Insurers such as U.S.-based PruHealth have a healthy living rewards program, known as Vitality, which gives points for healthy activities such as regular gym attendance and not smoking. Points earned from the rewards program can also be redeemed for other lifestyle rewards such as cinema tickets or gift certificates.

***

Big data has the potential to create sophisticated risk models that are focused on individuals, extremely accurate, and capable of being updated in real- time. This is bad news for those hoping to use insurance as a means to justify excessive risk taking, but it is good news for those that want to be rewarded for managing risk more effectively. As more and more individuals opt for dynamic risk management, society will benefit from safer roads and smaller healthcare bills.

This post is adapted from the book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at http://www.bigdatarevolutionbook.com

Share this:

CONVERSATION

2 comments:

  1. This blog failed to mentioned that Actuaries should start learning how to code (like R/SAS/Python/SQL) otherwise Computer Science Majors (like myself) will dominate in this field which we will.Can an Actuary explain what is Machine Learning? Or explain what algorithm(s) should the actuary should use for Data mining? Let's be realistic.

    There will be a huge clash between Actuaries and Data Scientists. Data Scientist's salary-wise is already skyrocketing while Actuaries salaries are stabilizing... The roles and/or responsibilities among the two are very similar except of course...the Actuarial Exams.

    My Background: Have a Masters in Computer (area of focus is Data Mining), Bachelors in Computational Mathematics and Statistics. Still working passing on the SOA Exams.

    ReplyDelete
    Replies
    1. Actuaries do code. A quick google search containing the terms 'actuarial' and 'r or python' will show plenty of resources geared explicitly towards actuarial science and the programming required to do the work.

      And of course actuaries know which techniques to apply. There is currently a semantic divide between what is referred to as 'data mining', 'maching learning', and 'data science', all of which are applied statistics. (I challenge you to provide a definition of the differences.) The same algorithms are being applied to the same fundamental problems of prediction and inference in different fields. Computer scientists say 'features and weights' while statisticians say 'variables and parameters'. Here's an excellent post on this phenomenon: http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/

      The takeaway: don't let buzzwords fuel your understanding of an industry. Actuaries apply statistics to the modeling of financial risk. In order to succeed, they must use statistical programming languages, understand statistical algorithms, and know when to use them in the context of the problems they are solving.

      Excellent article, in my opinion.

      Delete