While most carriers collectively understand that predictive analytics is necessary to remain competitive with other data-driven and VC-backed companies, progress isn’t as fast as it should be. Some are still not using analytics at all despite knowing its importance, while many others limit use to one area of the business. One main obstacle is a disconnect between the C-suite and analytics staff.
The C-suite will not invest in initiatives they cannot measure, manage and understand. Therefore, how a business handles the implementation of analytics is just as important as the predictive models themselves. This is why applied analytics programs are needed to push the insurance industry into the future.
Applied analytics recognizes the value of data in specific business processes and basing key decisions off the insights. Carriers must define their strategy and goals around the initiative, decide on an implementation approach and convey it successfully to the rest of the organization to ensure buy-in and adoption. Executing this can be difficult, often a result of balancing sound technical decisions against each individual organization's company culture.
Strategy and Goals
The first step a carrier should take when applying predictive analytics is building the strategy and selecting the goals. Beginning with smaller projects is often an excellent way to build confidence for future implementations across different areas of the business. In this phase, carriers decide which specific challenges they hope to tackle using predictive analytics. For example, a carrier’s main goal may be to improve risk selection and pricing, and a secondary goal would be to achieve underwriting consistency as the carrier grows its existing business in new states. By defining what the organization hopes to gain from a particular initiative, it avoids misunderstanding and confusion during the implementation and duration of the project.
See also: 3 Key Steps for Predictive Analytics
Next, the carrier must choose their success metrics. Using the same example, if the target for a predictive analytics initiative is improving risk selection and pricing, loss ratio improvement is often the best measurement. If business growth is the primary concern, profitability and aligning price-to-risk is an important metric to use. The C-suite is results oriented and this step sets the foundation for the implementation that follows.
Implementation Plan
Once a project has been selected, its goals determined and success metrics defined, an implementation plan should be created to strategize the role governance will play within the corporate culture. The way a carrier must address this step largely depends on the type of predictive analytics initiative in place. If a commercial auto carrier uses analytics to improve pricing with its long-haul trucking business, it’s key to find the balance between the model score and your underwriters’ expertise. Don’t leave it to chance, carriers should develop clear rules for how to use the predictive model that matches each company’s weaknesses and strengths.
By using the predictive model, policies are scored individually and assigned to a specific risk “bin”.
As one example, a carrier may decide that the best performing risks for smaller policies (bins 1-3) are approved for straight through processing, the average risks (bins 4-7) must always be reviewed by underwriters, and the poorer performing segments (bins 8-10) should be avoided altogether. A common and more granular form of implementation is determining how much credit or debit can be added to each policy depending on the model score, before needing management approval. By implementing rules of engagement that correctly fit a specific organization, it will not only boost the effect of the predictive analytics project but make it easier to manage and make necessary adjustments long-term.
Organization Adoption
Even if a carrier has an excellent strategy and model, it means nothing if those who will be using it on a daily basis fail to do so correctly. During the implementation process, all members of the organization must be aligned with project goals and comprehend its importance for the company. This means undergoing training and support by those involved closely with the initiative -- whether the model is being built in-house, through a third party vendor or a consultant. There should be complete transparency throughout the process, and room for adjustment based on feedback from the staff. Predictive analytics is an imperative tool in its own right, but just like any tool, it requires a skilled individual to obtain the best results. In fact, Valen research shows the best results are found when combining human judgement and predictive analytics.
The graph shows the lift of a predictive model. The greater the lift, the more effective the model is for the carrier. The blue line represents the loss ratio improvement based on a combination of the underwriter and the model when making decisions on pricing policies. There is clearly a more significant lift here when compared to both the underwriter (green) and predictive model alone (red).
See also: An Opportunity in Resilience Analytics?
In order to sustain long-term plans and goals, the predictive analytics strategy must converge with the overall corporate strategy. That can’t happen for any real length of time without executives confidently making important decisions using the insights that come from predictive modeling. Only when a carrier and all of its members fully understand those insights and trust in data are they able to become a data-driven organization.
