Property & casualty (P&C) insurers acknowledged that the capture and transformation of data into useful information has turned into a critical differentiator of performance within the P&C insurance marketplace, according to a new survey by global professional services company Towers Watson (NYSE, NASDAQ: TW). Executives from both personal and commercial lines insurers participated, with a relatively even divide among small, midsize and large carriers.
“Where there is data, there is opportunity,” said Brian Stoll, director, Property & Casualty practice, Towers Watson. “P&C insurers face difficult long-term competitive challenges, but these can be mitigated for carriers that efficiently integrate predictive modeling and data-driven analytics into operating functions across the enterprise.”
The P&C Insurance Predictive Modeling Survey clearly illustrated the importance of predictive modeling to insurers’ business. Personal lines carriers nearly unanimously (98%) said predictive modeling is either essential, or very important, to their business . Eighty percent of small to mid-market commercial lines carriers agreed. Large commercial accounts and specialty lines carriers were less convinced overall, with 55% indicating that predictive modeling is essential or very important to their business.
Competitive Advantage, Usage VariationThe desire to improve profitability emerged as the leading reason why P&C carriers use predictive models. Ninety percent of all U.S. participants cited a desire to improve bottom-line performance as the primary reason, followed by competitive pressure (75%). Larger insurers are actively using predictive modeling in the pursuit of competitive advantage, while smaller carriers have been slower to adopt it.
- For example, among personal auto carriers, 92% of large respondents use predictive modeling, a number that drops to 76% for midsize carriers and 57% for small carriers.
- The trend also holds true to a lesser extent for standard commercial lines such as commercial auto, where the percentage of respondents that currently use predictive models decreases from 62% for large carriers to 6% for small carriers.
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