The survey of European life insurers showed that over 80% of companies that already use predictive analytics report a positive business impact – with none reporting negative outcomes. In competitive markets, these techniques can provide insurers with a price advantage; in less competitive markets, they can lead to material cost savings. 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.
The impact of predictive analytics on top and bottom line performance has been greatest in relation to revenue generation, with 68% of insurers agreeing that it has had a positive impact on increased sales and cross selling.
Four out of ten companies (41%) reported that predictive analytics helped reduce issue/underwriting expenses and claims costs.
The survey finds, however, that while some life insurers are making progress with their predictive analytics capabilities, most companies still have significant challenges to overcome, including a number of strategic capacity issues:
• 30% of respondents believe that their analytics and/or actuarial teams lack the capacity to accomplish their predictive analytic goals.
• Data quality and reliability issues (58%), as well as infrastructure and data warehouse constraints (42%), with many in-house facilities stretched by large volumes of data that require greater processing power to handle the analysis,
• Most respondents (77%) still use traditional environments (desktops, servers, mainframes) for analytics, though one third are currently exploring cloud-based options which could allow greater flexibility.
Alastair Black, Director, Insurance Consulting and Technology, Willis Towers Watson, said: “Life insurers are on the cusp of real transformation, increasingly aware that by making predictive analytics a core corporate capability they can lay a strong foundation for profitable growth and high performance.
“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 analyse such vast amounts of data are just as essential if insurers are to harness its full potential.”
The survey found that the primary drivers for using predictive analytics are in-force management (69%), for example improving client retention; improving customer experience (67%); and seeking competitive advantage through product innovation and pricing sophistication (53%). Predictive analytics is currently used most within the individual risk business (32%), but the largest growth is planned in the savings business (31% over the next two years).
The global pandemic has also served as a catalyst for multinationals in particular to adapt and adopt greater usage of predictive analytics, with 36% of multinationals surveyed saying that they would now increase their use following the COVID-19 outbreak.
“The future of insurance is digital. We believe the winners will be those organisations that apply digital technologies to be connected, analytic and agile. And if the incumbents cannot get this right then new entrants will,” commented Black.
|
