A recent survey of North American insurance carriers revealed approximately 82% rely on predictive modeling and analytics to help with their product lines. Modeling provides strong analytical forecasting that insures a company is correctly pricing its lines of business and evaluating its risk. Predictive models also play a large part in expansions for companies looking to grow their books or potentially move into other lines.

Many companies are touting predictive analytics as a best practice for rating, essentially eliminating a great deal of the uncertainty that was attributed to a very manual underwriting and actuarial process. That being said, the data is only as good as the processes and platforms that are being used to analyze it. Predictive analytics and business intelligence are often referred to interchangeably; however, business intelligence is a subset of predictive analytics, and not necesssary the complete science of analytics.

Carriers have always relied on forecasting. In the case of property and casualty insurance in Cat exposed areas, earthquake, hurricane and tornado losses are often modeled using historical damage data and loss data to determine the potential risk of writing business in geographical areas that are potentially prone to large losses. This enables companies to determine their appetites in areas where they can write higher premiums but are prone to more risk.