The property and casualty insurance industry has experienced a surge over the past decade in the use of sophisticated predictive models, with increasing applications across all insurance lines of business.
These models have proven to be highly effective in optimizing insurers’ operations. However, despite their potential, many insurance companies are not meeting their potential in evolving their predictive modeling strategies, missing out on significant opportunities and untapped value.
At the outset of the use of predictive modeling in the insurance industry, carriers primarily focused on developing pricing models for large lines of business. Recognizing the importance of expertise in analytics and data science, insurance companies invested in acquiring talented professionals to build these models. Recognizing the knowledge gap that existed at the time, internal research and development teams were formed to effectively create and deploy predictive models.
The implementation of predictive models requires different skill sets compared to those possessed by data scientists. While data scientists excel at developing models, they may lack a deep understanding of the overall business drivers and needs. “As a result, even R&D teams that are efficient in creating models that meet a business need may struggle with effectively operationalizing them.
It is crucial to understand that predictive models are a tool to address a business problem, not a solution in and of itself.
Below we’ve outlined five considerations to ensure that your predictive models make a significant impact on your business and deliver maximum value:
1. Tackle significant business problems: First, make sure you have a clear understanding of the part of your business that you would like to impact with predictive analytics. Define this in concert with the R&D team. They will be able to confirm the ability of a predictive model to help, and will come out with a solid understanding of the goal.
2. Have a plan of action: Before proceeding, formulate a clear plan for implementing and managing the predictive model. Understand how data will be fed into the model, how to integrate model results into core systems, and ensure that the insights derived are actionable and aligned with the organization’s goals.
3. Continuously monitor performance: Once the model is created and implemented, be sure to monitor the performance. Understand that unexpected developments and consequences are common, and adjustments may be necessary to maintain the model’s effectiveness over time. Proactively identify and address issues to optimize its performance.
4. Be prepared to rebuild: Even a well-thought-out model may require larger revisions as the business and environment evolve. By monitoring results and seeking feedback from the field, it becomes apparent when further adjustments are inadequate and the model needs to be rebuilt. Continuous monitoring and improvement allow a more efficient process than simply rebuilding your models every three years.
5. Speak with data scientists: Data scientists and modelers have specialized skill sets that can be hard to come by, so it pays to use them strategically. Besides the creation of the predictive model itself, use those skills to scope the problem and design the solution, and to evaluate the model performance as necessary to ensure it continues to deliver value to the organization. For other tasks, not only are business analysts and systems professionals more efficient resources, but they are also more skilled in the implementation tasks required.
Insurance companies that carefully consider these factors will realize that predictive models are just one component of a comprehensive approach to problem-solving. It is essential to focus on the underlying business problem rather than solely relying on the predictive model itself. By adopting this approach, insurers can effectively leverage the power of predictive modeling to drive meaningful results.
As research and development operations become more efficient, you may naturally desire to apply predictive models to a wider range of business purposes. To enhance the robustness of predictive model applications, consider adopting technology platforms that enable the innovation, building, and scaling of predictive models. Look for solutions that seamlessly integrate with your core systems, as this facilitates automated data collection and analysis. By automating these data processes, insurers can streamline their operations, improve efficiency, and unlock the full potential of predictive modeling.
Refine the development and execution of predictive models to open up opportunities for continual improvement in insurance processes and performance. This continuous improvement can lead to a significant competitive advantage. By leveraging the insights derived from predictive models, insurers can optimize their underwriting, pricing, and claims processes. This, in turn, enables them to make data-driven decisions, identify emerging risks, and provide enhanced customer experiences.
After developing and innovating predictive models, the next critical area is to operationalize or embed those models into core areas of the business and workflows.