This is the second in a series of articles by Valen Analytics looking at the hurdles that insurers must overcome to effectively implement and gain value from data analytics programs.
After deciding to implement predictive analytics, insurance leadership will likely need to address the operational issues of bringing an analytics program from strategy to production. One such hurdle for the C-suite is encouraging underwriters to adopt new data solutions to improve their understanding of risks across a book of business.
Executive SummaryThe C-suite has some challenges ahead in finding ways to encourage underwriters to adopt predictive analytics in a way that helps them better understand risks. Kristin Marr, president of Valen Analytics, writes here about a number of things executives can do to boost underwriter buy-in of the technology.
Barriers such as institutional culture or fear of being replaced can lead underwriters to work around systems, or even avoid them altogether. While largely inaccurate beliefs may manifest this behavior, these concerns can be overcome by executives that take the time to target their analytics initiatives on measurable performance metrics and include all stakeholders in each step of the implementation process.
1. Early Inclusion
There are two steps that insurers can take early in the process of rolling out analytics programs.
The first step to achieving full buy-in is to identify underwriting team members with a robust knowledge of the book of business, but also interested in trying something new. Institutional knowledge is critical for understanding variances between the suggested pricing of a model, and the historical way an insurer has priced a policy. Studies show that insurers using analytics outperform those who are not, but a combination of data and individual knowledge yield the strongest results.
Once a team is in place for the pilot program, it is critical to communicate and celebrate successes that result from predictive analytics programs. If an insurer can improve the bottom line and grow top-line revenue by winning the types of business that fall into its risk appetite, this information should be disseminated across the team and organization.
It’s important for the developers and IT team building analytics solutions to understand the steps an underwriter takes before creating a policy. To do so, IT can perform interviews or even serve as “daily shadows” to underwriters, which allows them to see common approaches to understand how and when exceptions to typical workflow may occur. This information should be used to build the data analytics process or solution to lower the barrier to entry for underwriters to incorporate new data.
2. Provide Line of Sight
Aligning an organization around analytics initiatives means effectively communicating the goals for a program at the onset and explaining how a model aligns with meeting those goals. For example, if an insurer wishes to grow their workers compensation business into new states, predictive analytics can help identify adequate pricing for policies despite never writing business in the location before. When the necessary historical data is not available for underwriters, analytics is a powerful tool giving them greater confidence that they can hit their target goals.
Another key consideration is conveying how models can empower underwriters. Some underwriting teams are restricted in the amount of credit they are permitted to offer, whereas using a model will increase their flexibility. The use of analytics is meant to improve the work environment for underwriters, not replace them.
3. Build Feedback Loops
4. Forget About the Machines
Aside from finding the right test groups and giving them the ability to provide the necessary feedback, insurers must remember that their underwriting staff needs to undertake a mindset shift to fully incorporate predictive models in their day-to-day workflow. It can be tough to implement new approaches for individuals who have been working the same way for years, both in insurance and in most other industries. However, through a phased rollout that celebrates successes and addresses concerns, insurers can secure acceptance across their underwriter base. This will ultimately lead to increased growth, lower claims, and a book of business that more accurately aligns with an insurer’s risk appetite.