Why data can move insurance from protection to prevention

Insurers are poised to play a valuable new role as partners in accident prevention. As the frequency, scale, and costs of disasters increase, connected technologies such as in-home smart devices and third-party data sources allow insurers to deliver real-time, personalized alerts and insights that help policyholders proactively protect their homes, vehicles, and other insured assets. Many insurers already have fledgling IoT projects and growing stores of data from multiple sources that they can use to start delivering more meaningful connected insurance services. But adoption of existing connected products such as telematics-based car insurance remains low. Successfully using these connected insurance building blocks for prevention requires strengthening customer trust that insurers will use those insights sensibly, leveraging data effectively, and imagining new ways to protect customers from the worst impacts of disasters. 

Natural disaster costs

Because of changing weather patterns, natural disasters like hurricanes, floods, and drought-related wildfires are becoming more frequent. Between 1970 and 2019, the average annual number of major weather incidents increased fivefold, according to the World Economic Forum. In 2022, the U.S. endured "18 separate weather and climate disasters." That's more than double the average of 7 to 8 disasters per year between 1980 and 2021 in the U.S. reported by the Columbia University Climate School.

At the same time, these disasters are increasingly expensive. The 18 disasters in 2022 cost $165 billion, bringing the total for U.S. disaster costs since 2016 to more than $1 trillion, according to the NOAA's National Centers for Environmental Information. Weather-related disasters like flooding are also becoming more unpredictable. For example, a recent rainstorm in Fort Lauderdale, Florida, dropped about a third of the area's average annual rainfall on the city in eight hours, resulting in widespread flooding. Meanwhile, people and businesses continue to relocate to Florida and other Sun Belt states that are prone to severe storms, drought, and fire, which increases the number of people at risk of those natural disasters. 

The result is higher costs for everyone, from policyholders and taxpayers to insurers. Already, these pressures have driven some insurers to insolvency — six in Florida alone in 2022. As a result of rising costs and risks, insurers are taking steps to address higher potential payouts, including raising premiums and deductibles, and not writing new policies or renewing existing ones in especially risk-prone markets. 

Leverage data for prevention

The emerging area of connected insurance (CI) has the potential to help insurers prevent, mitigate, or lower potential risks relating to severe weather events. CI draws data from multiple sources, including connected / smart devices and sensors in homes as well as third-party data sources like weather forecasts, to deliver highly personalized recommendations that can help customers keep their homes and themselves safer.

Many insurers already use data for a number of means, from identifying personalized coverage options for customers to analyzing risk across insurer portfolios. A September 2021 survey also found that "35% of insurers have gained competitive advantages from their investments in data and analytics, such as growth in premiums written and improvement in loss ratios." Forty-three percent of property and casualty (P&C) insurers in the survey said they're using data and analytics to drive a shift in focus from protection to prevention. Almost a third (30%) of the commercial line insurers in the survey said they offer data-driven products to inform customers of real-time risks such as severe weather alerts. 

Adding sensor data to the mix allows insurers to add even more value. At least one commercial P&C insurer uses IoT sensors to track the functionality of customers' machinery, monitor fire risks, and monitor the structural soundness of customers' buildings in earthquake zones. This data is shared with customers so they can take steps to reduce their risk. This approach can also extend to personal lines. For example, data and insights from in-home fire, heat, and flood sensors could allow insurers to alert customers to fires, HVAC equipment failure, or basement flooding. Moisture sensors could detect leaks early enough to not only prevent major pipe breaks but also the spread of costly mold damage inside walls early on. 

Building such insight-based service would enable carriers not only to deliver better customer value, but also drive down their cost base by preventing claims from happening in the first place and/or handle incoming claims faster and more accurately – reducing cycle time, claims leakage and ultimately Loss and LAE ratios.

Creating data-driven prevention programs

Establishing an insights-driven prevention program, whether it's built from the ground up or on top of existing data programs, takes time and a thoughtful strategy. For example, which risks have the most potential for prevention-related loss mitigation and which customer personas are most likely to want prevention products? Insurers also need high quality data at scale and intelligent analytical capabilities to create prevention services that deliver real customer value. This requires five key steps: 

• Unify internal data and integrate relevant data from agents, brokers, and reinsurers. Getting data out of silos is the first critical step toward successful analytics and prevention products. 
• Establish data governance, stewardship and ownership within the organization. A hub-and-spoke method with a central team and business-unit level use cases can ensure that data is properly maintained and leveraged to its full potential. 
• Create a data culture across the entire organization. All employees should receive the training they need to understand and work with data to solve business problems, uphold data ethics, and maintain data security. 
• Identify your use-cases for products you want to build, define the necessary data/data sources to service that product and create the necessary analytical models to help you actually create that product.
• Embrace collaboration for an open-data ecosystem. Working with InsurTechs and other data ecosystems can help insurers achieve the scale they need for optimal analytics, especially for identifying patterns and making predictions around extreme weather events. 

In a risk landscape with more frequent events and ever higher costs, data-driven connected insurance has the potential to blunt the impact of natural disasters on customers and insurers by preventing damage, reducing payout costs, and positioning insurers as trusted partners to help their customers navigate an increasingly unpredictable environment.