This is the third 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.
Executive Summary
Shotgun use of predictive models across all new business in new territories and lines is a recipe deteriorating underwriting results, notes Valen Analytics President Kirstin Marr. Here, Marr outlines two other common pitfalls: relying on in-house data, potentially missing buckets of opportunity; ignoring stats, such as higher-than-usual bind rates, which are signs of trouble.When the pressure’s on to grow your business, it’s tempting to look for quick answers.
For insurers looking to expand into new states, new classes or new lines of business (LOB), there’s the added pressure of minimizing the new business penalty that often comes from the lack of experience writing in those segments.
Hiring underwriters from companies that are established in the space increases institutional knowledge, and combing through the state filings of competitors provides basic information about pricing structure. However, each of these approaches can be limited in terms of effectiveness. Throwing headcount at a problem can be a short-term solution but isn’t scalable. State filing information lacks all of the nuance that separates successful insurance companies from those with huge loss ratios.
