As the effects of climate change make themselves increasingly apparent in our world, businesses and individuals alike are rising to the occasion, in ways big and small, to help protect the environment.

Executive Summary

Data helping insurers to respond more effectively to climate-related events not only includes weather data from external sources, which can be analyzed with artificial intelligence, but also internally gathered data about customer preferences for receiving predictive alerts. Predictive analysis of wind speed, temperature, satellite and rainfall data can also help carriers allocate resources before major events, observes Sapiens Product Strategist Amir Raskin.

With the frequency of climate-related disasters on the rise, insurers can no longer avoid addressing the impact of a warming planet on their underwriting, pricing and overall business practices, and need to onboard the tools that will enable them to do so. Big data analytics, real-time and streaming data integration, risk management models, and predictive analytics are some of the tools available today for insurers, and if used right, they will be able to hit the sweet spot among affordability, awareness and availability.

It’s Raining Data

The weather and its shifting patterns have always played an influential role in how the insurance industry measures and predicts future risk. But many insurers still rely on outdated, historical data to do so. In a world where carbon emissions have changed our weather systems to be drastically different compared to the patterns of the past, insurers must adapt. Without knowing what to expect in the coming years, how can insurers appropriately plan for future losses?

Fortunately, recent advances in weather science, analytics, geolocation and cloud computing have given insurance companies the ability to harness a range of tools that generate valuable weather-related insights.

For example, artificial intelligence can analyze new real-time data sources such as wind speed, temperature, constantly updated satellite images and rainfall to predict future losses for policyholders within a specific area or weather system. One way in which insurance providers can tap into the power of relevant data to enhance their underwriting abilities is by connecting—in an integrated and easy-to-reach manner—with data partners who follow trends closely and who can regularly share their up-to-date insights. Take Meteomatics, a weather data company that has partnered with InsurTechs and insurers to deliver high-quality weather models. Meteomatics’ InsurTech partners have been able to utilize new, external data to bring context and perspective to their existing data, resulting in new powerful predictive capability for the climate change age.

Society in a Changing Climate

Collecting the necessary data to implement change is only half the battle. Insurers must also strategize how best to use this data to help their customers on the front end.

In the wake of traumatic environmental events—floods, forest fires, hurricanes or tornadoes—insurance companies are often the first ports of call. Armed with relevant predictive data and the ability to affect real change, insurers can identify the potential for a significant weather-related event and notify customers to take whatever steps are necessary to best protect their lives and their property.

These predictive alerts can be tailored in a variety of ways: different methods of warning notification for customers based on demographics and personal preferences; unique actions or preventative methods based on geography; identification of which critical at-risk assets should be prioritized in a time of need. Being proactive and personalized in the deployment of predictive alerts stands to offer significant savings for both insurers and those they protect.

Following a particularly significant meteorological event, improved predictive data can also be used to identify affected parties and prioritize the allocation of relief efforts or resources. By knowing which policyholders in an affected area may have sustained damages and contacting them directly, representatives can quickly begin claims processing for individuals who have endured the worst losses.

Important information about customer behavior can be gleaned through both pre- and post-event touchpoints, lending insight into contact preferences as well as product and service needs. For insurers, it can be as simple as linking this information to other information sources or marketing data to gain a more in-depth view of their customers’ unique individual needs. Using these detailed insights, insurers can even go as far as to create new products, develop cross-selling campaigns, and tailor collateral design or development to unique customer segments.

Claims and Policy Creation

The back end of any insurance operation also stands to benefit from a climate-conscious approach. Simply put, insurance companies need to change how they work with people. Better policies come not only from better data and analytics but from a team that knows how best to utilize them.

In the best-case scenario, insurers can use advanced analytics and reliable predictive data to reduce weather-related claims or flag suspicious ones, anticipate customer needs and reduce time to payment, and appropriately allocate resources to address customer needs before a major event even takes place. In addition, insurers can adjust their financial assets based on this up-to-date and precisely calculated reserve and other aspects of the company’s financial assets.

Looking Forward

Climate change is the issue of our current and future generations, and its acceleration must be heeded by leaders across industries.

Technological innovations are empowering insurers to improve their decision-making processes—and as a result, their customer experience and financial KPIs—within all aspects of their operations. Incorporating data into a variety of different use cases and insurance activities can be the difference between prevention and reaction when it comes to events that are unavoidably unpredictable.