How are life insurers planning to use big data and predictive analytics?

Big data has captured the attention of companies in most industries, including life insurance. Extracting useful insights from big data requires careful planning and execution of advanced analytical techniques and technologies. To be successful, insurers must have the right people, systems and processes in place.

P&C paved the way with advanced analytics

The property and casualty (P&C) insurance industry is further along in using advanced analytics to improve risk selection and offer customers new products. For example, a growing number of auto insurers now offer usage-based insurance products that use technology to monitor driving behavior and reward good driving with a discount. Tech-savvy Millennials, in particular, find these products appealing and believe usage-based insurance is a better approach for policy underwriting than traditional methods, such as credit scores. (For more on this, see my colleague Klayton Southwood’s post, How P&C Insurers Are Using, or Plan to Use, Big Data.)

With so much excitement about the potential in this area, the key for life insurance carriers is to learn from the expertise acquired by the P&C insurance industry. In particular, life insurers need to think ahead about how they want to use and deploy big data and predictive analytics. 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. Chart your course, set goals and then invest in the areas that will be needed to succeed.

Life insurance just getting started with big data

In a life insurance context, data sources that are already being used or explored include

  • administrative systems
  • claims data
  • website clickstreams
  • medical records
  • prescriptions
  • credit scores
  • social media

While many life insurers are just getting started, most expect their use of big data and predictive analytics to increase dramatically over the next couple of years, according to a Willis Towers Watson survey of life insurance CFOs and senior executives. Many life insurers also anticipate expanding data applications and looking to new data collection sources.

Figure 1. Many life insurers are just getting started

Figure 1. Many life insurers are just getting started

Bold plans for the future

Over half (58%) of respondents say they only know a little about big data and predictive analytics or understand the basics; none consider themselves experts. Currently, 42% think of themselves as very knowledgeable.

Respondents expect this to change in a big way. While only 8% say they are actively using data to assist in decision making, over the next two years, 62% plan to rely on big data and predictive analytics to help them actively make decisions in many business operations. While this is a bold undertaking, those committed to moving forward can succeed with the right people and systems in place. In particular, life insurers will need to conquer infrastructure limitations, investment dollars and competing demands.

How will big data and predictive analytics transform the future?

More than half of carriers responding (53%) currently apply big data and predictive analytics to increase market penetration. However, two years from now, life insurers expect to use big data and predictive analytics to

  • transform their business model,
  • expand customer relationships,
  • enhance the customer value proposition
  • improve internal performance management
Figure 2. Top future uses of big data and predictive analytics

Figure 2. Top future uses of big data and predictive analytics

Data sources are evolving

A challenge for all companies entering the big data universe is what to collect, where to collect it from and what to do with it when you get it. Life insurers responding to our survey were queried on their current data collection sources — both internal and external — and what they have planned for the next two years.

Presently, the top internal data collection source is administrative systems (100% of survey respondents), followed by

  • claim data (77%)
  • agents (55%)
  • underwriting data
  • ZIP code data (both 46%)

These will continue to be reliable internal sources of data, but life insurers say they will harvest from additional sources in the future, including emails, dwebsite clickstreams and agent/customer voice-to-text logs.

External data collection sources are also expected to expand. Life insurers we surveyed are currently using permitted external information from medical records (72%) and prescription data (61%). Within the next couple of years, they plan to mine intelligence sources such as credit scores, websites and social media. The Internet is a gold mine with ripe potential for enhancing customer knowledge and relationships.

Figure 3. Current data collection sources

Figure 3. Current data collection sources

Figure 4. Future data collection sources

Figure 4. Future data collection sources

Barriers and challenges to using big data

Life insurers recognize that in order to realize the potential big data offers, they must first address a number of barriers and challenges. The biggest barrier identified in our survey is infrastructure limitations (71% of respondents). Financial constraints were also cited by over half (54%) and a lack of knowledge and expertise ranked third.

Half or more survey respondents also pinpoint top barriers to harnessing big data as conflicting priorities, data availability (both 54%) and people, including resources, training, skills and capabilities (50%).

Figure 5. Big data barriers and challenges

Figure 5. Big data barriers and challenges

Unlocking the potential of big data and predictive analytics

Although life insurers are still in the early days of realizing the full potential of both big data and predictive analytics, many have their eyes on the prize and are taking aggressive steps forward. Commitment will be as important as technology. The most successful companies will attract and retain the right people, develop an integrated strategy across business functions and invest wisely in reliable technology.

 

This post was originally published on the Wire June 6, 2016.

 


Elinor FriedmanGuest blogger Elinor Friedman manages Willis Towers Watson North America life insurance consulting practice.

Categories: Analytics, Financial Services | Tags: ,

2 Responses to How are life insurers planning to use big data and predictive analytics?

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