Property & Casualty (P&C) insurers say that although they are increasingly comfortable with the impact predictive modeling has on their business, they also acknowledge its implementation is uneven, lacks uniformity, and differs by line of business and carrier size. Global professional services company Towers Watson's Predictive Modeling Survey illustrates the variety of different predictors that personal and standard commercial lines carriers report as valuable, and the inconsistent application of predictors to the various operational aspects of risk management.Nearly all (92%) personal lines insurers say predictive modeling remains essential to performance success for risk selection and rating, yet under half (44%) of standard small to mid-market commercial lines insurers say so, and only 56% of large commercial and specialty lines insurers indicate it's essential or very important. "The effectiveness and extent of modeling implementation is a strong indicator of whether these applications are realizing their full value and contributing to insurers' profitability," said Brian Stoll, director, P&C practice, Towers Watson. "Beyond national carriers, insurers are only starting to understand the potential of a comprehensive program that applies data-rich analytics to a wider range of insurance functions."
More than half (57%) of insurers use predictive modeling for underwriting and risk selection, and its implementation is expected to grow by 33 percentage points over the next two years. The long-term growth trend for modeling techniques is consistent across all areas of insurers' business, with carriers planning to use it for fraud identification (36%), evaluation of litigation potential (46%) and loss control (49%). "The real value of predictive modeling/data-driven analytics is their potential to impact results across multiple functions, quantify disparate costs and create a risk assessment framework that enables insurers to pursue calculated risk-taking opportunities," said Klayton Southwood, director, P&C practice, Towers Watson.
Over half (56%) of respondents that consider their companies data-driven say they use predictive modeling for other functions, compared with just 12% that are not data-driven - a critical divide, since skillful collection and analysis of data can help insurers boost profitability through more accurate pricing, increases in operational efficiency, and more effective marketing and sales, noted Stoll. "Now that almost two-thirds (65%) of insurers have advanced to where they consider their companies data-driven, these companies are more likely to gain a competitive edge in the marketplace, putting the remaining third of carriers at considerable risk."
The survey found the average interval between updates of insurers' models was shortest for personal lines: 1.9 years for both homeowners and auto. For other lines, including specialty, commercial, general liability and workers compensation, the average was between 2.3 and 2.6 years. "The refreshing and updating of models is an important aspect of successful execution of predictive modeling applications," said Southwood. "The greater use of modeling for specific applications leverages its benefits, but unless pricing models are reviewed regularly, the effectiveness of these models can diminish."
About the Survey
Towers Watson conducted a web-based survey of U.S. and Canadian P&C insurance executives from September 3 through October 22, 2014. The results represent the views of 52 U.S. insurance executives. Responding companies represent a significant share of the U.S. P&C insurance market for both personal lines carriers (17%) and commercial lines carriers (22%).