Predictive analytics play a prominent role in the insurance industry today, and the benefits to the organization from proper utilization are plentiful. But despite the relative importance of analytics, many carriers still ponder where it's best put to use, who is ideally suited to spearhead and maximize the success of the effort, what is the potential impact and what resources are being used to accomplish this. To answer these questions, Insurance Networking News teamed with Mark Gorman, principal, Mark B. Gorman & Associates LLC, Minneapolis, to conduct exclusive research that queried property/casualty and life insurance carriers to uncover how insurers are using predictive analytics and business intelligence to create a more holistic predictive analytics decisioning methodology.
The breadth of carriers' responses came from companies of all sizes, with 56% of P&C respondents having fewer than $500 million in net written premiums, and 33% of life companies under $100 million. This is telling to Gorman, as he believes that interest and involvement in predictive analytics runs much broader in terms of carrier size than many people currently believe.
Based on the results, a general level of consistency emerged between life and P&C carriers in terms of how they're utilizing predictive analytics, but at the same time, there also were discernable differences. For example, when predictive analytics are being used from a pricing, underwriting, new business and risk assessment standpoint, life and P&C insurers are similar in terms of the percentages of companies with initiatives either being utilized or in production (with the highest percentages being underwriting performance). The area with the highest percentage of initiatives under development for P&C carriers was business segmentation. For L&A carriers two areas are most under development, pricing precision and product innovation. "This is a very interesting impact, especially given what many people decry as the commoditization of the insurance market," Gorman says.
The survey revealed more predictive analytic solutions in production for fraud by life insurers than P&C carriers, but Gorman says that if you add in the initiatives under production for both life and P&C, they're relatively consistent in terms of their approach to fraud. When asked about use in claims processes, 67% of P&C companies said they're currently using predictive analytics, with the majority of those respondents citing salvage and subrogation (62%) and adjuster assignment (54%) as the most common claims processes that they currently have in production.
The results also indicated a great deal of development has been made in the area of governance, risk and compliance. "It's clear predictive analytics has made an impact on insurers' operational and transactional areas (claims and underwriting), but people have recognized - and are recognizing - the power of analytics in making better decisions," Gorman explains.
WHO'S IN CHARGE?
In more than 40% of responses (42% for life and 44% for P&C), the CEO is considered the champion of implementing and developing predictive analytics programs. Gorman says this is key, as it shows organizations are approaching predictive analytics adoption in a centralized manner, and that predictive analytic resources are an enterprise-level asset. Line-of-business management was considered the second-largest driver of analytics organizationally, and the CFO ranked third. "CFO involvement tells me that insurance companies are clearly seeing this as a strategic capability that has financial impact and drives financial involvement," says Gorman.
It's worth noting, on the life side, 50% of respondents also believed that actuarial is a primary champion of predictive analytics within the company (compared to 28% of P&C companies). Based on these responses, Gorman espouses that analytics are implemented within an organization for two reasons: Insurers are driven by an operational need and a business owner at the LOB level, and insurers see the strategic benefit so this is a commitment that the CEO is making. "What's emerging as the key success factor is how rapidly the organization adopts a culture of looking at data driven empirical decisions as opposed to holding on to the old culture of making 'gut-level' decisions based on the experience of personnel" he says, citing findings from additional research he's performed on the subject.
"How quickly they get to that culture is highly dependent on how fast they adopt, and how quickly they leverage predictive analytics as an organizational asset," Gorman continues. "It's not clear today, in the insurance market, whether the fastest way to get there is through CEO involvement or line-of-business involvement. It appears to have everything to do with the strength and persuasiveness of the champion."
As far as whether insurers are using internal resources, external resources or a combination of both, the overwhelming majority of life respondents (greater than 40% for both life and P&C insurers) are using predominately internal resources for everything from building the business case, to selecting a vendor, to building the predictive models and providing support once the project goes live.
For both life and P&C carriers the second highest approach is a joint effort with internal and external resources. Very few respondents (less than 19% across the spectrum) said they were turning any of these functions over solely to a third-party. Of the two groups, life insurers reported being much more comfortable (an average of 23.5%) using a third-party resource as a stand-alone to accomplish their needs - most specifically around building and validating the predictive models.
Gorman says it will be interesting to see the impact in terms of how quickly organizations adopt and then spread the use of predictive analytics. Normally, he says, this could be an indicator of insurers recognizing initially they don't have the internal expertise, and they want to move quickly and respond to the market; after which they pull the capability in-house, or it could mean there is a resource squeeze and they need to turn externally simply as a matter of not having enough internal resources to properly manage the situation.
While there really weren't any huge surprises in the results, according to Gorman, he notes that one of the biggest takeaways has to do with issues surrounding insurers moving their culture to doing business in a new way. He stresses that insurers across the spectrum are starting to rely more and more on the data and market-based information predictive analytics provides to either augment human decisions on a broader basis, or to automate the decisions depending upon the results.
Following along with this shift in culture, Gorman asserts that for any of these changes to be made, it requires broad support within the organization.
"I'm finding in my independent research, that true changes are made from the middle out," Gorman says. "No matter what happens-either from a line of business perspective or from a senior manager or CEO saying 'this is what we're going to do' - it's when the middle managers who are responsible for operations or functional business units grasp and embrace predictive analytics that it really takes off."
(c) 2008 Insurance Networking News and SourceMedia, Inc. All Rights Reserved.
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