Last year, U.S. P/C insurers incurred a $21.2 billion net underwriting loss, only slightly improved from a $24.9 billion underwriting loss recorded in 2022. Roughly $65 billion in natural catastrophe losses hit P/C insurers last year. The August 2023 Maui wildfires alone contributed an estimated $4-$6 billion in damages.

Executive Summary

Following up on his prior opinion piece, “It’s No Longer Enough Just to Insure,” Peter L. Miller, president and CEO of The Institutes, writes that predicting and preventing catastrophic events, as well as the day-to-day risks, is critical to the economic sustainability of insurers. In addition, he suggests that if there are ways to prevent the devastation of events like wildfires in Maui last year, then members of the risk management and insurance community “have an ethical and moral responsibility” to do so.

As the severity and frequency of losses continues to increase exponentially, so does the cost to repair damages after a loss occurs. This is making insurance’s traditional approach of detecting and then repairing after a loss no longer economically viable.

Predicting and preventing catastrophic events, as well as the day-to-day risks we encounter, is critical to the economic sustainability of insurers. It is also crucial to helping our customers avoid catastrophes. If you’ve seen images of the aftermath of the wildfires that hit Maui or the Texas panhandle you would also agree that if there was a way to prevent this devastation from occurring again, we, as members of the risk management and insurance community, have an ethical and moral responsibility to act on it.

The Proven Economics of Predict and Prevent

Hartford Steam Boiler introduced the concept of predicting and preventing downside risk in 1866, when it introduced boiler inspections as part of its insurance value chain. Insurance was a secondary financial benefit to the safety and loss prevention provided by the inspections. HSB knew that it was economically more attractive to prevent a boiler accident than incurring the cost to repair it afterward.

Also on Carrier Management: HSB: The Original InsurTech; Creating the Tipping Point for Insurance IoT: How HSB and Its Partners Are Creating a Playbook for the Future

The predict-and-prevent approach is even more critical to economic viability in today’s business environment because, among other factors:

• There is not enough capital to continue to rely on a detect-and-repair model.
• The costs to repair losses are increasing exponentially.
• The frequency and severity specifically of climate risk is escalating rapidly.

If average annual loss costs are around 60 percent of expenses, plus an average 10 percent of expenses from loss adjustment costs, that is about 70 percent of annual expenses that could be significantly lowered or even eliminated by a predict-and-prevent approach. (Percentages were estimated based on this March 7, 2024 Carrier Management article, “U.S. P/C Industry Underwriting Loss Reaches 10-Year High: AM Best“)

Leveraging a predict-and-prevent approach can also increase accessibility to needed coverages. For example, reducing the volatility of losses could help to reduce or eliminate premium spikes during hard markets and reduce the frequency of insurers having to make tough financial decisions, such as no longer providing homeowners coverage in states like Florida and California when rates can’t keep up with unchecked risk exposures.

Technology Enabling Predict and Prevent

The economic viability and necessity of moving to a predict-and-prevent approach is clear, and thanks to the confluence of maturing Internet of Things devices, artificial intelligence and data storage/sharing technologies, the approach is already being implemented in a variety of ways to prevent losses.

What this means is that we have access to more data than ever before (from IoT and sensors), we have AI tools to enable us to make sense of the data faster than we ever have before, and we have advanced ways of sharing and storing the data, such as on a blockchain.

Insurers also have a unique advantage—the entire insurance value chain is built around hundreds of years of insurers collecting, interpreting, storing and leveraging risk data to deliver on the promise of insurance. With this background in data, and transformative technology reaching a level of maturity, insurers are empowered more than ever before to glean insights, make predictions and take preventive actions with unprecedented precision and efficiency.

Broad Spectrum of Risks

Here are examples of how predict and prevent is already being applied, and in some cases how insurers are partnering with tech companies to prevent their insureds from suffering losses:

• Cyber Risk: AI-driven, adaptive anomaly detection systems can sift through vast volumes of data to identify suspicious patterns indicative of potential cyber attacks, enabling organizations to fortify their defenses before breaches occur.
• Personal Lines: Smart sensors in homes can sense spikes in electrical activity, predicting possible electrical fires and preventing them by immediately notifying the homeowner and providing support for what to do.
• Commercial Lines: Water sensors in commercial properties can trigger an alert at the start of a leak and can also turn off water lines immediately to avoid devastating property damage from flooding.
• Workers Compensation: IoT sensors worn by employees, as well as computer vision in a workplace, is being used to analyze warehouse employee movements, such as unsafe lifting, to alert and educate on safer lifting practices and to prevent injuries.
• Business Interruption: Predictive analytics can forecast demand fluctuations or logistical bottlenecks within the supply chain, empowering companies to preemptively adjust inventory levels or optimize routes to ensure uninterrupted operations.
• Climate Risk: AI, in combination with a variety of different technologies, is being used to prevent destructive wildfires by scaling precise fuel treatment plans for high-risk areas, enabling safe mitigation approaches; AI-enhanced flood projections are offering improved scenario planning to help avoid catastrophic flood damage.

Outside of the typical risk management and insurance sphere, other organizations are leveraging this paradigm shift, as well. For example:

• The NFL, working with Amazon Cloud Services, implemented a program to predict and prevent player injury by leveraging sensors in players’ pads and helmets, and a network of AI-powered cameras to capture action on the field.
• NASA is working on developing lighter, safer aeronautical batteries for flight to predict if an aircraft is going to overheat to prevent catastrophic in-flight failures.

A Win-Win Approach to Carrier Self-Preservation

For carriers, embracing predict and prevent isn’t merely a matter of choice; it’s a necessity for self-preservation in an increasingly volatile and interconnected world.

At a high level, here’s what’s needed for carriers to get started with their own predict-and-prevent approach:

• Robust data governance frameworks to ensure the integrity, privacy and security of the vast volumes of data fueling AI models.
• Investments in talent and technology (or partnerships) to cultivate data science expertise and deploy sophisticated AI-driven solutions effectively.
• Fostering a culture of innovation and collaboration to leverage cross-functional insights for holistic risk management.

No sector is better positioned to deliver on the value of this approach than the risk management and insurance disciplines, yet the reality is, if insurers don’t move to predict and prevent, other industries will take on this challenge and write the rules of engagement.