Use of Analytics Pervades P&C Carriers

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While underwriting, claims and product development are the main areas of analytics usage for property/casualty insurers, a significant number are now using analytics to gain insights on customers, finance and operations, according to “Data and Analytics in Insurance: Property and Casualty Plans and Priorities,” new research by Mark Breading, partner, Strategy Meets Action.

On average, P&C insurers spend 9 percent of their IT budgets on data and analytics, and an equal amount is spent by business users coming from other departmental budgets, the study found; 75 percent of P&C insurers said they will increase their spending on analytics over the next three years.

“We really see analytics as the competitive battleground for the insurance industry,” Breading said. “Everything really centers around how you leverage the data you have and the data you can acquire, and the main message is that it’s not just about risk. Insurance from its very origins was data-centric; looking at information to understand what is the risk, how should we price it. Now there is a whole lot more that insurers are doing with analytics, and analytics is spreading out across the entire value chain.”

With the exception of claims, personal lines carriers, by pushing into predictive analytics, tend to be more advanced than commercial lines carriers. The study also found that large organizations tend to emphasize claims, while smaller organizations tend to invest more in marketing.

For both commercial and personal lines carriers, the study found the top business areas for analytics are the same: underwriting, to better understand and manage risks; product development, for actuarial, pricing and loss reserving; and claims/payouts, for fraud detection and catastrophe planning.

The differences were more pronounced based on the insurers’ size, the study found. For P&C insurers with revenues more than $1 billion, 78 percent were investing in underwriting; 58 percent in claims/payout; 48 percent in product development and 34 percent in both marketing and policy/servicing. For those with revenues of less than $1 billion, 69 percent were investing in underwriting; 47 percent in claims/payout; 53 percent in product development; 46 percent in marketing and 36 percent in policy/servicing.

For personal and commercial lines carriers, more money is being spent to understand customers, build brand awareness and create sales leads. For personal lines carriers, 46 percent are using analytics for customer segmentation, 10 percent are implementing and 11 percent are piloting, compared with commercial lines carriers, with 44 percent using, 7 percent implementing and 11 percent piloting. Other notable marketing applications are ‘single view of the customer’ and campaign analysis.

“The winners over the next decade are those who figure out how to use analytics for customers, products, finance and investments, for claims, you name it,” Breading said.

Analytics are used heavily for product development by both personal and commercial lines carriers, with three-quarters already using the tools for actuarial analysis, and just fewer than 15 percent piloting and implementing them. The numbers were similar for pricing models and product performance.

Roughly one-quarter of all P&C insurers are piloting or implementing analytics for underwriting, and just less than 60 percent are currently using them. The numbers are similar for risk analysis. For distribution, roughly half are using analytics for channel/agent performance, and 20 to 23 percent are piloting or implementing. One-third are using them for CRM, and an additional 20 percent are piloting or implementing them.

More than half of all P&C insurers are using analytics in their claims operations, and more than one-fifth are piloting or implementing them; the numbers are similar for catastrophe modeling; roughly 40 percent are using them for fraud prevention and 15-to-20 percent are piloting or implementing.

The study also identified the barriers to capitalizing on analytics, including:

Lack of strategy

Data accessibility

Legacy core systems

Lack of overall priority or funding

SMA also examines traditional types of business intelligence, including the market penetration of reporting, dashboards and scorecards, ad-hoc queries, analysis tools and scenario planning; and advanced analytics, including advanced statistical analysis, data and text mining, predictive analytics, models and collaboration.

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