Earnix, a leading provider of integrated pricing and customer analytics solutions for banking and insurance, and ISO, a leading source of information about property/casualty insurance risk, today released the results of a joint industry survey: 2013 Insurance Predictive Modeling Survey. ISO is a member of the Verisk Insurance Solutions group at Verisk Analytics (Nasdaq:VRSK).
With the objective of helping insurers learn from the experience of their counterparts, Earnix and ISO conducted the survey to uncover how predictive modeling and analytics are used throughout the industry. Responses were collected online from 269 insurance professionals representing companies that sell personal and commercial coverage in Canada and the United States.
The survey results reveal widespread use of predictive analytics in the insurance industry, with as many as 82 percent of respondents currently using predictive modeling in one or more lines of business, including personal auto (49 percent), homeowners (37 percent), commercial auto (32 percent), and commercial property (30 percent). According to survey respondents, predictive analytics enables insurance companies to drive profitability (85 percent), reduce risk (55 percent), grow revenue (52 percent), and improve operational efficiency (39 percent).
While the use of predictive analytics is pervasive throughout the insurance industry, larger insurance companies are more likely to make use of predictive modeling than smaller ones. In fact, all the respondents from companies that write more than $1 billion in personal insurance use predictive modeling, compared with 69 percent of the smaller companies that took part in the survey (writing less than $1 billion in personal insurance).Here are additional key findings:
- Top challenges mentioned by respondents include lack of sufficient data and limited numbers of skilled modelers.
- Using additional data attributes is the most promising avenue seen by survey respondents to increase the power and quality of models built today.
- The most common use of predictive analytics is for pricing, where 71 percent of respondents use predictive modeling either always or frequently.
- Companies spend considerable time on data preparation and deployment before and after actual modeling work. More than half of survey respondents (54 percent) spend more than three months on data extraction and preparation, and more than two-thirds of the respondents (69 percent) take more than three months to deploy new models.
- The role of big data in modeling initiatives is predominantly a big company affair at this point. Of the companies with more than $1 billion in gross written premium (GWP), 51 percent either currently use big data or are evaluating or implementing big data initiatives, compared with 30 percent of the companies with less than $1 billion GWP.