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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.”
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 Variation
The 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.
“Smaller carriers slower to adopt predictive models are at a disadvantage, particularly those competing for business directly against the large insurers,” said Klayton Southwood, senior consultant, Towers Watson. “They face loss of market share and adverse selection as their larger counterparts that have implemented predictive models can target better risks and price more accurately. Smaller insurers need to find ways to follow quickly and leverage predictive modeling despite their relative lack of scale.”