P&C insurers are doing a good job understanding the value of and building capabilities for predictive modeling. That's according to the 2016 Predictive Modeling Benchmark Survey (U.S.) from Willis Towers Watson, released this week.

The company says that two-thirds of P&C insurers surveyed currently use predictive models for underwriting and risk selection, a 10% increase year-over year. While companies across business lines tend to agree "about the fundamental importance of using more sophisticated predictive techniques to drive success in today’s market," commercial lines insurers tend to indicate greater interest on expanding their capabilities.

That's because the universe of data that could support commercial activities has been limited in the past, the report continues. "Many commercial lines carriers are recognizing that the traditional barrier of the relative paucity of homogenous risk data in commercial portfolios can be overcome, enabling models to contribute significantly in more unique underwriting environments," Willis Towers Watson writes. "Commercial insurers have ambitious expansion plans in areas such as claim triage, fraud potential, litigation potential and case reserving."

The top challenge to implementing predictive analytics continues to be staffing, the report adds, with more than half of respondents citing that issue. Next-level concerns include capturing the right data (37%), cost considerations and funding (35%) and data quality and reliability (33%). Most insurers plan to retrain existing employees to solve the staffing problem than outsource by a three-to-one ratio.

“We’re nearing the point where market momentum will accelerate, as value-building big data, and diverse and growing analytics techniques take hold. Insurers that want greater control over their destinies will marry flexible IT frameworks and partner with the analytics techniques and skills necessary to effectively harvest data,” says J.J. Ihrke, senior consultant and actuary, Willis Towers Watson.

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