A small commercial underwriting vice president attends a predictive modeling seminar, does some additional research, and comes to believe implementing a model is critical to the business’s success. However, when faced with broaching the idea to the organization, the VP balks.

That’s understandable. The prospect of moving from the idea of implementing predictive modeling to actually doing it can pose a whole host of unknowns, such as:

  • Will the company’s leadership understand the basics of predictive modeling and support its use?
  • Is there enough data available to build a predictive model, and what data sources should be used?
  • Does the organization have the necessary expertise to build a predictive model, and if not, what options exist?
  • How long will it take to design and build a model?
  • Will the potential benefits justify the investment of company assets and resources?
  • What will front-line underwriters and, more importantly, agents think?
  • How does one measure success with a predictive modeling project?

If these are the same questions you have, and they’re stopping you in your tracks when it comes to implementing predictive modeling, you’re not alone.

Why small commercial is slow to adopt predictive modeling

While predictive modeling has proven itself to be an invaluable risk assessment tool in personal lines insurance, adoption within the small commercial insurance sector has been relatively slow. Typically, one or more of the following perceived stumbling blocks are to blame:

  • A lack of resources
  • A lack of understanding about how to build an effective model
  • Concerns about engaging the organization in the process

Luckily, there are some simple best practices that can get your business over all these hurdles so that you can enjoy all the benefits of predictive modeling, including reducing your company’s vulnerability to risk and growing your business. What’s the key? Leveraging concepts from the product development lifecycle process.

Paralleling the product development lifecycle

Generally, when it comes to predictive modeling development, carriers typically find themselves in one of these four situations:

  1. Lacking the expertise or data to build and incorporate their own models into the operations workflow.
  2. Capable of building predictive models, but unable to incorporate them into the operations workflow.
  3. Unable to build their own models but adept at implementing vendor or consultant built models.
  4. Sophisticated at building and implementing predictive models within their operations.

Regardless of which of these situations applies to your business, by paralleling the structured four-stage product development process of ideation, design and development, implementation, and monitoring, any carrier can successfully create a predictive model that will bring additional benefits to the underwriting workflow.

In my next two posts of this three-part series, I’ll explain how.

For additional information about predictive modeling best practices, please see our whitepaper, Making Predictive Modeling Work for Small Commercial Insurance Risk Assessment.