“In competitive markets, these techniques can provide insurers with a price advantage; in less competitive markets, they can lead to material cost savings,” Willis Towers Watson said. “With the help of predictive analytics, historical and real-time data can also be mined to anticipate industry trends and customer needs, enabling insurers to exceed customer expectations.”
Sixty-eight percent of insurers surveyed agreed that predictive analytics had a positive impact on increased sales and cross-selling, and 41% reported that predictive analytics helped reduce issue/underwriting expenses and claims costs.
However, the survey found that while some life insurers are making progress with their predictive analytics capabilities, most still had “significant challenges” – including several strategic capacity issues:
- 30% of respondents said that their analytics and/or actuarial teams lacked the capacity to accomplish their predictive analytics goals
- Many respondents cited data quality and reliability issues (58%) and infrastructure and data warehouse constraints (42%), with many in-house facilities strained by large volumes of data that require greater processing power
- 77% of respondents still use traditional environments (desktops, servers and mainframes) for analytics. One third are currently exploring cloud-based systems, which could allow greater flexibility, Willis Towers Watson said
“Life insurers are on the cusp of real transformation, increasingly aware that by making predictive analysis a core corporate capability they can lay a strong foundation for profitable growth and high performance,” said Alastair Black, director of insurance and consulting technology at Willis Towers Watson. “Implementing these new approaches can be a complex process. Insurers will need to pick business-use cases wisely and identify the most effective way to use data. Having the right talent and tools to process and analyze such vast amounts of data are just as essential if insurers are to harness its full potential.”
The study found that the main drivers for using predictive analytics are in-force management (69%), improving customer experience (67%), and seeking competitive advantage through product innovation and pricing sophistication (53%). Predictive analytics is currently most widely used within the individual risk business (32%), but the largest growth is planned in the savings businesses (31% over the next two years).
The COVID-19 pandemic has also spurred multinationals in particular to adopt greater usage of predictive analytics, with 36% of multinationals surveyed saying they would increase their use following the outbreak, Willis Towers Watson said.
“The future of insurance is digital,” Black said. “We believe the winners will be those organizations that apply digital technologies to be connected, analytic, and agile. And if the incumbents cannot get this right, then new entrants will.”