The one constant in insurance is that there will always be new innovations and trends that suddenly disrupt the industry.

Executive Summary

John Beal, senior vice president, Analytics, LexisNexis Risk Solutions, explains how advanced analytics can help insurers with everything from segmentation to scoring, pricing, risk assessment and claims processing.
Some of the newest disruptive forces include the Internet of Things (IoT), especially telematics, wearables, smart homes and connected vehicles. While carriers have always had to manage large volumes of data for underwriting, they have never had to deal with it on such a massive scale before. It is prompting carriers to ask “Am I getting the most from my data?”

Without advanced analytics, the answer is no. Carriers need to integrate advanced analytics to take advantage of this new tsunami of data to improve segmentation, scoring, pricing, risk assessment and claims processing. More data breeds new attributes. More attributes are the basis of predictive modeling. And accurate predictive modeling yields faster, smarter decisions through advanced analytics. It gives a carrier a more holistic view not only of its own business, but of the industry to better prepare for and adapt to the next big disruption.

The opportunities for leveraging advanced analytics span all the touchpoints across the insurance continuum – acquisition, underwriting, pricing, claims and renewal. Analytics can greatly reduce the cost, complexities and processing times of these customer touch points.

Using analytics to facilitate correct assessment at the point of acquisition and underwriting can help reduce errors in pricing risks, a problem that leads to higher loss ratios, lower profits and unhappy customers. Analytics can help manage underwriting and claims activity more efficiently. It can speed up claims handling, resulting in more low- or no-touch claims and reduced settlement amounts through more accurate severity estimation. Analytics can also be used to better identify fraud earlier in the claims cycle, among other uses.

Analytics Improves Customer Experience in Multiple Ways
Let’s consider an example of using advanced analytics for acquisition, segmentation and channel selection. One of the nation’s largest property and casualty insurers wanted to improve sustainable growth by cross-selling and up-selling mono-line customers to bundled policies. It struggled to garner a profitable rate of auto-to-property-policy conversions among its customers because its typical or traditional marketing campaigns cast a wide net, resulting in low returns and wasted resources.

Using predictive analytics, the company was able to develop a new property-to-auto cross-sell campaign, which achieved a 246 percent increase in policy conversion. Analytics revealed insightful segmentation opportunities such as improved responder profiles, which enabled the carrier to predict how likely auto consumers would respond to a property quote invitation. Once the analysis was complete, the carrier designed two cross-sell campaigns that offered auto policyholders the opportunity to save money on premiums by adding a homeowner’s policy to their existing auto coverage. Campaign response rates increased by 7 percent, yielding huge gains in new policies written. Beyond the new revenue opportunities, the carrier was also able to contain costs, streamline critical business processes, and improve their return on marketing spend.

Another use for analytics is customer retention. With industry attrition rates exceeding 20 percent, a continued analysis of its book of business can help a carrier maintain a positive customer experience to minimize the temptation to shop for alternative providers at the point of renewal.

One regional property and casualty carrier used a multi-sourced advanced analytics approach to build a predictive retention model that monitored policies nearing expiration and identified which policyholders were most at risk for non-renewal. Integrating a variety of new data sources not previously used by the carrier, the model created a new view of customer behaviors targeting the policies most likely not to renew. It then generated a retention score as well as the reason for the score for each customer, providing a quantifiable prediction of how likely that customer is to non-renew and why.

With this data in hand, the carrier developed customized messaging to address the relevant reasons for renewing or not renewing a policy, such as price or service. This enabled the carrier to target campaigns to the most profitable customers. Campaign strategies were also modified for maximum effect (for example, some customers only required a mailer where others also received a phone call). The carrier ultimately saved more than $2 million in potentially lost premiums in the first year of the program.

Analytics and Connected Care Data

Combining advanced analytics with connected care and connected home data is one of the most exciting new opportunities for assessing and managing risk. Telematics, for example, powers usage-based insurance (UBI), which shifts the traditional technique of pricing based on an approximation of individual risk (from a sample population) to pricing based on specific individual risk (from an individual’s driving behavior). It can help carriers understand which channels attract preferred risks, resulting in more effective channel management. Telematics data can also change claims handling processes to reduce fraud exposure.

Similarly, applying analytics to new data generated by smart building technologies lets carriers become more of an advisor to customers, proactively managing risk rather than just responding to it. New data points being generating by these new IoT innovations include intruder alarms, fire detection, smart thermostats, lighting controls, smoke/CO2 detectors and smart appliances. With these new streams of data come new potential for risk intelligence and mitigation.

How advanced analytics impacts the claims-processing touch point continues to unfold. Predictive analytics can significantly minimize fraud costs by detecting more fraud, curtailing false positives, and reducing fraud investigation expenses. The technology can also help lower claims handling costs, more quickly identify outlier claims, and better manage claims severity.

Predictive analytics has given property casualty carriers a viable fraud defense system capable of turning the tides of medical claims fraud and drastically reducing its devastating losses. In addition to looking at individual bills as they come in, advanced analytics enables carriers to access a range of intelligence based on years of data related to providers, their practices and their claims histories. It delivers a big-picture view of providers that reveals patterns that are impossible to recognize from the bill-level point-of-view.

In the near future, wearables like smart watches and activity trackers offer another IoT-driven option to provide personal medical data that may factor into both claims and risk assessment as well.

Final thoughts
Many decisions across the insurance continuum can be improved with advanced analytics. New disruptive innovations are creating massively more data that must be tamed and normalized so that it is useful and usable. Carriers need to leverage every bit of data and every new advancement across the continuum in a way that will help insurers maintain a competitive advantage. Those who embrace advanced analytics will be better able to flex to the technological disruptions and improve interactions with customers at every touchpoint, ultimately making them more competitive and profitable

Contributor

John Beal, LexisNexis Risk Solutions

John Beal is senior vice president, Analytics for LexisNexis Risk Solutions.