Majority of North American insurance companies use predictive analytics to enhance business performance, new Earnix/ISO survey shows
NEW YORK and JERSEY CITY, N.J., November 4, 2013 — Earnix, a leading provider of integrated pricing and customer analytics solutions for banking and insurance, and ISO, a leading source of information about property/casualty insurance risk, today released the results of a joint industry survey: 2013 Insurance Predictive Modeling Survey. ISO is a member of the Verisk Insurance Solutions group at Verisk Analytics (Nasdaq:VRSK).
With the objective of helping insurers learn from the experience of their counterparts, Earnix and ISO conducted the survey to uncover how predictive modeling and analytics are used throughout the industry. Responses were collected online from 269 insurance professionals representing companies that sell personal and commercial coverage in Canada and the United States.
The survey results reveal widespread use of predictive analytics in the insurance industry, with as many as 82 percent of respondents currently using predictive modeling in one or more lines of business, including personal auto (49 percent), homeowners (37 percent), commercial auto (32 percent), and commercial property (30 percent). According to survey respondents, predictive analytics enables insurance companies to drive profitability (85 percent), reduce risk (55 percent), grow revenue (52 percent), and improve operational efficiency (39 percent).
While the use of predictive analytics is pervasive throughout the insurance industry, larger insurance companies are more likely to make use of predictive modeling than smaller ones. In fact, all the respondents from companies that write more than $1 billion in personal insurance use predictive modeling, compared with 69 percent of the smaller companies that took part in the survey (writing less than $1 billion in personal insurance).
Here are additional key findings:
- Top challenges mentioned by respondents include lack of sufficient data and limited numbers of skilled modelers.
- Using additional data attributes is the most promising avenue seen by survey respondents to increase the power and quality of models built today.
- The most common use of predictive analytics is for pricing, where 81 percent of respondents use predictive modeling either always or frequently.
- Companies spend considerable time on data preparation and deployment before and after actual modeling work. More than half of survey respondents (54 percent) spend more than three months on data extraction and preparation, and more than two-thirds of the respondents (69 percent) take more than three months to deploy new models.
- The role of big data in modeling initiatives is predominantly a big company affair at this point. Of the companies with more than $1 billion in gross written premium (GWP), 51 percent either currently use big data or are evaluating or implementing big data initiatives, compared with 30 percent of the companies with less than $1 billion GWP.
“Earnix and ISO share a belief in the power of advanced analytics, so we’re very pleased to collaborate in a joint effort to help insurance companies in North America assess their predictive analytics capabilities,” said Meryl Golden, general manager of North America Operations at Earnix. “The results show that the use of predictive analytics is, and will likely remain in the future, a clear priority for insurers seeking to better understand current and future risk and improve their decisions related to pricing/rating, underwriting, marketing, and claims.”
“ISO is proud to work with Earnix on this revealing and useful study,” said Phil Hatfield, vice president of Operations at ISO Innovative Analytics (IIA), a unit of ISO focused on advanced predictive modeling solutions for the property/casualty insurance industry. “The survey confirms that the industry has recognized the value of predictive analytics but still faces challenges in this area. Data inefficiencies, scarcity of analytic talent, and the cost of that talent can hold companies back from completing as many initiatives as they would like. And smaller carriers often have significantly fewer resources to dedicate to modeling. However, there are innovative predictive modeling analytics tools available in our industry that all carriers can adopt to make better business decisions.”
Complete results of the 2013 Insurance Predictive Modeling Survey can be found at http://earnix.com/iso-predictive.
Earnix Integrated Pricing and Customer Analytics software empowers financial services companies to predict customer risk and demand and their impact on business performance, enabling the alignment of product offerings with changing market dynamics. Earnix combines predictive modeling and optimization with real-time connectivity to core operational systems, bringing the power of analytic-driven decisions to every customer interaction. Banks and insurers rely on Earnix solutions to improve deposit, loan, and insurance policy offerings. For more information, visit www.earnix.com.
Since 1971, ISO has been a leading source of information about property/casualty insurance risk. For a broad spectrum of commercial and personal lines of insurance, the company provides statistical, actuarial, underwriting, and claims information; policy language; information about specific locations; fraud identification tools; and technical services. ISO serves insurers, reinsurers, agents and brokers, insurance regulators, risk managers, and other participants in the property/casualty insurance marketplace. ISO is a member of the Verisk Insurance Solutions group at Verisk Analytics (Nasdaq:VRSK). For more information, visit www.iso.com and www.verisk.com.
Earnix provides an advanced analytics platform designed for the financial services industry, which integrates real-time decision-making capabilities into the business process, delivering significant results.
Earnix’s modeling, algorithms, and Machine Learning capabilities automate the rapid deployment of customer-centric offers by considering variables such as price, product features, and distribution channels, to optimize KPIs such as revenue, profit, sales volume, and customer satisfaction.
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