Over the next two years, many property/casualty insurers expect to advance their use of predictive analytics to both improve business performance and leverage big data, according to Willis Towers Watson, which adds that this seemingly makes sound business sense since as more than four-in-five insurers already using predictive models have seen a positive impact on their profitability.
The company’s annual predictive modeling survey found that even with insurers continuing to apply predictive modeling techniques to the tried and true areas of underwriting and risk selection, carriers anticipate significantly increasing model use across a number of other important business areas. For example, just 17 percent of respondents are currently using predictive modeling for claim triage, but more than half (52%) intend to join them in the next two years; only 10 percent utilize modeling for evaluation of litigation potential today, yet half (51%) plan to do so within the same timeframe.
Insurers also project their big data usage to grow across many business functions. Presently, big data is most useful for insurers’ work associated with pricing, underwriting and risk selection (42%). Respondents told Willis Towers Watson that big data is not significantly helping any other business area. In fact, the next highest function (product development) for which big data sources are being used to improve decision-making received less than 20 percent consensus.
However, insurers do expect this to change. Nearly half believe big data will benefit their company in two years in other areas including pricing, underwriting and risk selection (48%), making better management decisions (46%) and loss control and claim management (44%).
Usage-based insurance (UBI) information is the big data source that insurers think will increase the most over the next two years, followed by agent interactions via web, clickstream, phone and email data. Ten percent of respondents currently use UBI as a big data source, though this is expected to grow to 42% over the next two years. Similarly, merely two percent use agent interactions as a big data source now, but this is expected to grow to twenty-seven percent over the same time period.
“Insurers who embrace predictive modeling complexity by focusing on data enrichment, advanced analytics and technology can achieve a significant return on their investment,” said Klayton Southwood, director, P&C practice, Willis Towers Watson. “Carriers who catapult beyond their competition do so, in part, by leveraging superior data organization and analysis. For those insurers aspiring to unlock the potential of big data, they must be strategic, persistent and consistent.”
Even as enriching models with new data sources for broader application is ripe with potential, survey results illustrated some of the significant challenges insurers face. Half of the respondents indicated that people issues, such as resource availability, training, skills and capabilities, are their primary challenge to using big data. Data capture and availability ranks second (44%), as many carriers struggle with legacy policy administration systems that were not designed to capture and report data at the current needed level of granularity.
“Larger carriers have been more active in exploring big data applications that use both internal and external data,” said Southwood. “Smaller carriers will need to strategically assess their options, develop big data capabilities, and become fast followers of larger carriers when size and scale issues make using data from internal interactions unfeasible.”
Personal lines carriers remain the market’s predictive analytics leaders, though standard commercial lines and specialty lines carriers are steadily advancing model use. “Many commercial insurers have gained experience using predictive models as benchmarks for underwriting and pricing and are now starting to realize the value for modeling across all lines of their business,” the report says.
Willis Towers Watson’s 2015 Predictive Modeling and Big Data Survey was fielded from September 9 to November 2, 2015. Respondents comprise 11 percent of U.S. personal lines carriers and 17 percent of commercial lines carriers.
Source: Willis Towers Watson