Insurance Big Data
Recent Willis Towers Watson surveys in the U.S. have shown that P&C and life insurers in developed markets are taking seriously the potential of big data and predictive analytics to improve their businesses. Nimbleness and agility, rather than brute force, are likely to be key to realizing that potential.
Among the many things for which the late, great Muhammad Ali will be remembered is his famous catchphrase: “Float like a butterfly, sting like a bee,” referring to his strategy of using quick feet and ring craft to overcome sometimes more powerful opponents.
Faced with the current excitement (and, let’s be honest, degree of hype) about how big data and advanced analytics are set to transform the insurance industry, insurers would do well to take a leaf out of the inimitable Ali’s book.
Big data and advanced analytics will have a significant impact on the industry — of that there is little doubt. But it’s anyone’s guess how the industry will look in 10 to 20 years as a result. The dilemma for insurers is which direction to take and how fast and decisively to move, combined with the worry that competitors may beat them to the punch in key areas.
STRONG INTENT
In recent months, Willis Towers Watson has surveyed both the U.S. P&C and life insurance industries to see how companies plan to use big data and predictive analytics to keep pace and improve competitiveness. The findings reflect what we’re hearing from insurers in other developed markets and show a strong intent to make use of the opportunities that big data and analytics might provide.
P&C insurers typically already use predictive analytics extensively in their pricing. In the next two years, carriers expect to significantly ramp up model use in other important areas such as fraud prevention, claim triage, evaluation of litigation potential, and sales and marketing (Figure 1).
Figure 1. Top areas where U.S. P&C insurers expect to use predictive modeling beyond risk selection
*Survey fielded September 9 – November 2, 2015 Source: Willis Towers Watson 2015 Predictive Modeling and Big Data Survey
Complementing these approaches, P&C insurers expect big data use in many key business areas (such as claim management, understanding customer needs and product development) will more than double in the next two years. Expected sources of these data are both internal and external, including telematics, web clickstreams, customer-agent interactions, smart home data and social media, accompanied by a shift toward greater use of machine-learning techniques (see “The new era of insurance analytics: Driven by technology, toolkits and talent” on page 20).
By comparison, many life insurers are just getting started. Only 8% say they actively use big data and predictive analytics to inform decision making, and over half of life chief financial officers admit they know a little or just understand the basics about big data. Nonetheless, like their P&C counterparts, life survey respondents expect their applications of big data and predictive analytics to soar in the next two years; over 60% say they anticipate using them to support decision making across multiple business functions in that time frame.
More than half of life insurers are targeting increased market penetration, and other planned uses involve transforming business models, expanding customer relationships, enhancing the customer value proposition and improving internal performance management (Figure 2). Life executives also expect to augment current sources of data, such as administration systems, medical records and credit scores, with email, clickstream and social media-based information.
Figure 2. Top future uses for big data and predictive analytics among U.S. life insurers
*Survey fielded September 15 – October 13, 2015
Source: Willis Towers Watson 2015 Predictive Modeling and Big Data Survey
WHO, WHAT, WHY, WHEN AND HOW
The findings from the surveys tell us what kinds of things insurers are contemplating for big data and analytics and, to some extent, why. Just as important, of course, are the how, who and when.
All three questions present insurers with a number of common obstacles to tackle head-on.
A hefty 71% of life insurers in our survey said they don’t have the IT infrastructure necessary to execute plans for big data and advanced analytics (Figure 3). Today, the cloud and other on-demand sources of processing capacity may ease that pressure. However, a large proportion of respondents recognize the problems associated with the need to access and process potentially huge volumes of data, often sourced from legacy systems. Therefore, insurers need to find ways of making the transition to wider use of big data and predictive analytics relatively painless and cost-efficient.
Figure 3. Perceived barriers and challenges to use of big data
Moving forward effectively also depends on having the right people and the right culture to make the most of investment in this area. Advancing the understanding and use of big data and predictive analytics will require commitment that starts with attracting and retaining the right talent — data scientists and digital marketers, for example, with the knowledge and experience to inform and execute company strategy.
DATA CHALLENGES
Key challenges for all companies entering the big data universe are what dat