How P&C insurers are using, or plan to use, big data

Big data has become a topic of great interest in the insurance industry over the past few years. Data alone isn’t enough; big data is the focus. As we reviewed insurer responses to our 2015 Predictive Modeling Survey, it became clear that companies are focused on the importance of harnessing the power of big data in a predictive analytics framework to improve their decisions with respect to pricing, underwriting and overall business strategy.

Data-Driven Companies are on the Rise and Ahead of the Game

Sixty percent (60%) of U.S. property & casualty (P&C) insurance companies who responded to the survey characterized themselves as data-driven, and we identified several key differences between these insurers and the group of carriers that don’t view themselves as data driven. While the vast majority of respondents use predictive models for pricing and risk-selection, carriers who say they are data driven are much more likely to use analytics in other ways, including development of key performance indicators and performance dashboards by function, underwriting leading indicators and detailed management information.

For those carriers who didn’t characterize themselves as data driven, there are clear challenges related to the ability to store and access data that they need to overcome. For example, companies gave these reasons for not being data driven:

  • Warehouse constraints and access to data are hurdles (74% )
  • Data being difficult to integrate (63%)
  • Lack of sufficient staff to analyze data (53%)

These are clearly common challenges, but they are challenges that will need to be solved, because leading carriers will continue to get smarter about their decision-making and leverage their ability to outplay carriers that lag behind.

How Big Data Helps

One thing is certain: the industry has big plans for big data. Currently, the majority of respondents (42%) said that the primary areas in which big data is being applied are

  • Pricing
  • Underwriting
  • Risk selection

Future plans are much more aggressive, with carriers expecting big data applications to expand dramatically over the next two years to include helping with better management decisions, loss control and claim management, understanding customer needs and marketing/distribution/sales.


Big Data Sources

Of course, one of the big challenges is identifying big data sources and then determining how to collect and store it. Responses to our survey indicate that there will be lots of activity in gathering and applying big data from a number of new sources.

So far, the most common big data sources have been unstructured internal claim information and internal underwriting information (both identified by about 35% of respondents). However, in two years, insurers say that they plan to use a variety of additional sources, with the most significant usage increase in usage-based insurance (UBI)/telematics data, agent and customer interactions (especially Web, clickstream, phone and email data), smart-home data and social media.


But, having the data clearly isn’t enough, and the survey results indicate that many carriers are going to need to invest in their human capital as much as they are going to need to invest in their infrastructure.

Fully half of survey respondents say that their biggest challenge at the moment is people issues such as resource availability, training, skills and capabilities. Data capture and availability are the second greatest challenge. Organizational commitment is another hurdle many carriers will face, as one third of participants also identified conflicting priorities and cost considerations/funding as hurdles.


Unlocking Big Data’s Full Potential

The race to build a better mouse trap constantly speeds up and the sophistication used in building them is ever increasing, it seems. Harnessing the full potential of big data will involve focus and finesse. Superior data collection, organization and analysis will catapult the market leaders beyond their competitors. To get ahead, you will need to be strategic, creative and persistent in the search for new sources of big data and new ways to apply them.

This post was originally published April 22, 2016.

Klayton SouthwoodGuest blogger Klayton Southwood is a Director in Willis Towers Watson’s insurance consulting business and co-author of the company’s annual property & casualty insurance benchmarking study on predictive analytics. He currently leads the competitive market analysis practice in the U.S.

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