January 30, 2013 – Where there is data, there is opportunity, said Brian Stoll, director, Property & Casualty practice, Towers Watson.
Findings from a new Towers Watson survey indicate that P&C insurers are seeing those opportunities in their data; and according to the “P&C Insurance Predictive Modeling Survey” of small, midsize and large personal and commercial lines carriers, the capture and transformation of data into useful information has turned into a critical differentiator of performance within the marketplace.
Ninety-eight percent of personal lines carriers said predictive modeling is either essential, or very important, to their businesses, and 80 percent of small to mid-market commercial lines carriers agreed. Large commercial accounts and specialty lines carriers were less convinced overall, with 55 percent indicating that predictive modeling is essential or very important to their business.
Ninety percent of all 63 U.S. participants cited a desire to improve bottom-line performance as the primary reason, followed by competitive pressure (75 percent). According to the findings, most carriers have improved their bottom-line profitability through predictive model implementation; top-line growth impacts have been less pervasive. Personal lines carriers have realized more benefit in all measures of top- and bottom-line performance and at a faster pace than commercial lines. While most participants said they begin seeing revenue results in a year or less, the time to realize net income results is frequently two years, according to Towers Watson.
Midsize and large carriers reported significantly more favorable bottom-line impacts from predictive modeling, particularly in the areas of loss ratio improvement and profitability. Larger insurers are actively using predictive modeling in the pursuit of competitive advantage, while smaller carriers have been slower to adopt it.
While the benefits are plentiful, so are the challenges. All carriers said they struggle with data and people challenges when incorporating data modeling into rating or underwriting plans, but differences emerged by size of carrier. Nearly three-fourths (73 percent) of large carriers cited both data and IT resource constraints as their top two challenges, while 64 percent of midsize carriers ranked IT resource constraints and 61 percent listed cultural challenges as their biggest hurdles. “For large carriers, the sheer volume of customers, producers, data and business lines, combined with cumbersome legacy systems, may explain why data and IT resources present challenges,” Stoll said.
Seventy-five percent of small carriers ranked data as their biggest challenge, followed by people challenges (55 percent). “Smaller carriers report data challenges, but don’t voice as much concern about IT resource constraints. This may reflect how, by comparison, the challenge of making better use of their limited data dwarfs other concerns,” Stoll said.
“Working to establish consistency in underwriting and claim internal data capture electronically in defined fields, particularly for non-financial but potentially predictive variables, will position carriers to fully leverage their book of business in building better predictive models for risk selection, pricing and claim settlement,” Stoll tells INN. “For people challenges, communication and engagement throughout the process are critical in aligning key stakeholders behind predictive modeling efforts, and working to ensure common goals and robust execution once predictive models are implemented.”
Insurers’ top priorities for improving their modeling techniques differ by line of business, according to the survey. Personal lines carriers intend to spend more time focusing on non-pricing applications, such as development of target marketing lists. Many of these carriers are ready to extend the use of predictive modeling to gain market share in competitive consumer markets. Conversely, more than one-quarter of commercial lines respondents indicated that monitoring experience against modeling results will be a higher priority in 2013, in addition to enhancing modeling approaches.
Personal lines carriers are more likely to use models for automated renewal decisions and for pricing than commercial lines insurers. With respect to claim applications, personal lines carriers are more likely to use modeling to detect fraud, while commercial lines carriers use it more to triage claims or to evaluate claims for litigation potential. Dependent variable targeting differs, too, with nearly two-thirds of personal lines carriers modeling on frequency and severity separately, and less than one-quarter modeling on loss ratios.
To read more from the report, click here.
This story originally appeared at Insurance Networking News.
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