Refine Risk Assessment in Insurance with Profitable Underwriting

Profitable underwriting naturally rests on a carrier’s ability to accurately predict risk. The world’s most innovative insurance companies are using dynamic weather data to help them better understand the risk assessment in insurance they may face in coming years as a result of uncertainty about the climate. But how can insurers make confident business decisions based on data that they can fully trust?

Successful data-driven strategies require new ways of working. They call for complex transformational change encompassing entire business processes across multiple departments. To innovate for maximum impact, insurance carriers must look to the advances in meteorology of course, but they must also master the ability to leverage dynamic weather data for more accurate underwriting.

The Onward March of Technology: A Positive Trajectory

Two decades ago, you could only predict weather events at a relatively coarse-grained level, and with limited accuracy. Science and technology have evolved rapidly since then, providing far more granular data, reflecting what is happening at a much more localized level. Moreover, this information is available in real-time, giving innovative insurance carriers an edge with respect to both accuracy and timing.

Today, data science enables you to understand the real-world impact of weather events as they happen. You can, for example, see the exact path of a tornado, just moments after it has occurred. In the past that would have required on-site surveys after the fact. Today, it’s possible to have that same information immediately, and with greater accuracy and precision than ever before.

The Uncertain Impact of Climate Change

Over the last 12 months, the Intergovernmental Panel on Climate Change (IPCC) has published several reports synthesizing the best data and tools currently available. The goal is to develop a better understanding of weather extremes and how they are changing over time.

According to the IPCC report, the most recent decade (from 2011 to 2020) was probably the warmest in the past 125,000 years, and over the last 100 years, the temperature change has been more extreme than has occurred in nearly 60 million years.

The IPCC report also asserts that human-induced greenhouse gas emissions have led to increased frequency and/or intensity of weather and climate extremes. If true, that has significant ramifications for the insurance industry with respect to potential future losses. If insurers are to adequately serve their customers over the long term, it is critical that they understand the risks associated with climate change and appropriately price that risk into their policies. Failure to do so could put a carrier at risk, leaving them unable to offer such protections in the future.

The data shows, for example, that annual rainfall does not appear to have changed very much overall, yet the frequency of severe rain events has increased significantly in the northeastern United States. That region is seeing about 70% more frequent severe rain events, interspersed with an increased number of drought periods. While the overall amount of rain is roughly the same, the increased frequency of severe rain events results in a higher incidence of flash flooding and associated damage.

This is just one example of how risk assessment in insurance, new and different risks, may emerge and lead to an increased frequency and severity of losses. How can insurers adjust to this changing risk environment and make certain that they will be there for their policyholders over the long term?

Using Dynamic Weather Data to Better Understand and Model Risk

Technology is stepping in to help answer that question. Over the past 20 years, there have been significant advances in both weather technology and data analytics. By combining highly granular weather data, and historic data with powerful predictive modeling, insurance carriers can develop a better understanding of how risks might change over time. That leads to more accurate (and therefore more profitable) underwriting.

That’s not the only benefit, of course. Highly granular risk forecasting can help carriers accurately pre-position claims adjusters in advance of severe weather events. It enables them to provide policyholders with advance notice of impending risk, potentially helping them to avoid losses altogether. If customers can be proactively notified of a potential hailstorm, for example, they may have sufficient opportunity to park vehicles under cover and prevent them from being damaged.

Dynamic weather data can also help prevent losses by highlighting events that should prompt insurers to place temporary holds on issuing binders on new policies. Data analytics can also be a very powerful tool in detecting potential fraud.

The ability to accurately score individual properties for the type and magnitude of severe weather events is a game-changer. Machine learning makes it possible to extend the benefits of advanced data analytics to regions that currently have less weather-related infrastructure, giving you an even more comprehensive view of global weather trends and associated risks.

There are multiple potential long-term implications for improving the understanding of weather-related risks at a macro level. First, you are likely to see fewer cases of insurers going into receivership. If they can avoid high levels of risk exposure and, in some cases, reduce losses due to billion-dollar weather events, they’re less likely to experience catastrophic losses. That could take the form of diversification, higher premiums for policies in certain regions, or even the decision to stay out of specific markets altogether.

It will likely lead to better risk mitigation strategies over the long run. Construction standards in areas prone to flash floods, for example, may need to be adapted to limit or prevent losses in the face of such events.

By leveraging the power of advanced analytics with dynamic weather data, insurers can develop a better understanding of long-term risks, and they can also improve their underwriting practices, fraud detection, and customer service in the short term.

Precisely’s Dynamic Weather offers highly granular datasets covering an array of different risk factors such as wildfires, severe rain events, tornadoes, hurricanes, wind, hail, and earthquakes. Insurers can get up to seven years of historical weather, plus near real-time updates about current weather conditions in a hyper-local context. Dynamic Weather enables more effective analysis of past losses and improved risk assessment at both an individual policy level and across an entire portfolio.

If your organization is seeking to improve risk assessment in insurance, business performance in the short term and guard against the long-term risks of climate change, Precisely can help. To learn more, read our analyst report Refine Risk Assessment in Light of Climate Change for Profitable Underwriting.