It Started with a Hat Maker: Predictive Analytics in Life Insurance

September 19, 2016 by Anne-Frances Hutchinson

The practice of predictive analytics gave birth to life insurance. You could even argue that the basic way John Graunt assessed risk in the late 1600s isn’t that much different from the way life insurance risk is assessed today.

Things that make you go “hmmm” … all over again

In 1661, Graunt, a London haberdasher, analyzed rudimentary market research in an attempt to identify foot traffic patterns in local shops. His data? Century-old birth and death records mandated by the Church of England and kept locally by hundreds of parish clerics, each of whom recorded the information differently from one another.

Graunt sorted and analyzed that data, and found mortality patterns that he could reliably predict. (At the time, very little had been written about probability; prior to his work there had been no use for probability mathematics other than for gambling.) The result was the life expectancy table, created roughly 30 years ahead of astronomer Edmund Halley’s annuity tables.

The force of unintended consequences

The patterns Graunt found were infinitely more meaningful than the intended marketing study. In addition to tracking lifespans, he parsed the causes of death over a 57-year period, and was able to to illustrate the progression of the plague in London that no one had been previously able to see. With a single comprehensive study, Grault created the springboard for the development of the life insurance industry and the science of epidemiology.

From church spires to siloes

Facing the data challenge of the 1660s is theoretically similar to the challenges faced by life insurers today, especially in data capture and mining. We’re not analyzing handwritten data by candlelight and scribing paper ledgers bound by sheepskin and sumac, but many contemporary claims adjusters absolutely feel the ghost of Grault’s pain as they repeatedly search through deeply siloed and widely dispersed death benefit data.

Follow the P&C road

While there’s huge buzz about the potential for predictive analytics in the sector, most of the excitement centers on new product and business development and customer experience. In How Are Life Insurers Planning To Use Big Data And Analytics, Willis Towers Watson (WTW) recommends that the life sector follow the example of P&C producers to create roadmaps for the use of predictive analytics.

“In particular, life insurers need to think ahead about how they want to use and deploy big data and predictive analytics,” writes WTW blogger Elinor Friedman. “Some P&C insurance companies initially invested a generous amount on infrastructure and applications without as much consideration regarding how they wanted to deploy it in the market. It is critical for life insurers to think about how they will be able to use these tools.”

…until you hit the roadblocks

Friedman’s wise advice is tailor-made for companies with the infrastructure, culture and budget to “(C)hart your course, set goals and then invest in the areas that will be needed to succeed.” But, depending on the size and scope of an organization, that approach may be little more than a lofty ideal.

While the survey estimates that 53 percent of respondents are currently using data and predictive analytics to gain market share, 71 percent cited lack of infrastructure as the most significant barrier to adoption. Financial restraints and lack of knowledge follow closely behind.

According to WTW, the top three challenges facing life insurers when it comes to predictive analytics are

  • conflicting priorities (54 percent)

  • availability and quality of data (54 percent)

  • lack of people, resources, skills and capabilities (50 percent)

One hundred percent of the respondents identified administrative systems as their top data collection source, followed by claims data at 77 percent.

The passing of the “paper generations”

We know that life insurance data for subscribers born from 1946 until roughly 1984 were paper (or punch card) based. As the last “paper generation” dies off over the next few decades, the many benefits of using predictive analytics on the front end will become obvious.

Digital natives won’t be insisting on speed and accuracy any longer; those qualities will be an intrinsic part of information exchange.

For the foreseeable future, life insurance companies will still be managing paper-based data. For the most part, digital natives will be the beneficiaries of the millions of insured boomers who will reach their life expectancy in the next 20-plus years. Those natives will expect speed and accuracy when it comes to their survivorship and death benefits, and it’s the responsibility of life insurers to provide it – and soon.

At Captricity, we know the value of your legacy data, and we have a revolutionary approach to helping you get access to it. For 50 percent of the top insurers in the U.S., Captricity is the onramp to unprecedented data capture speed and accuracy. Our newest video shows how one of the top five life insurers uses our solution to increase productivity and efficiency:

Predictive Analytics and Legacy Data at New York Life

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