Viewpoint: AI’s Heroic Impact on Extreme Weather and NatCat Events

Hundred-year storms. Record wildfires. Unexpected tornados. These and other extreme weather and national catastrophe events are happening more often today due to climate change. While the impacts of climate change have been unfolding over the last several decades, they’ve intensified in recent years, heightening insurers’ risks and randomizing their exposure.

American insurers faced payouts of a staggering $108 billion due to natural disasters last year, according to Swiss Re, marking the fourth consecutive year of claims surpassing the $100 billion mark. There were 142 loss-inducing NatCat events, the most in a single year. Looking forward, the impact of climate change is only set to worsen, presenting insurers with a fast-changing landscape to navigate. Technology offers a holistic solution to meet these challenges head on.

AI in Insurance: The Hero on the Horizon

Extreme weather and NatCat events strain claims resources, escalate complexity, increase financial burdens due to large payouts, raise fraud risks and highlight the need for enhanced customer support. In response, insurers are not sitting still. They’re doubling down on their holistic, scalable, catastrophe response capabilities. But data-driven insurers are also stepping up, using advanced digital technologies to modernize their approaches to claims and future-proof their organizations.

They’re enhancing their claims processes by using AI for more precise risk assessments and modeling. They’re dynamically adjusting claims triage and prioritizing cases based on severity. They’re innovating with AI-powered damage assessments to improve settlement accuracy and reduce processing time. And they’re spotting fraudulent claims by detecting patterns and anomalies.What’s behind this progress? Artificial intelligence.

“As the frequency and intensity of natural disasters rise, the insurance industry is up against Goliath-sized challenges from Mother Nature,” says Suman Upardrasta, vice president of banking, financial services, and insurance at Everest Group, a global research firm. “The combination of deep expertise in both insurance and AI holds enormous potential to help insurers, insurtechs and the communities they serve become more resilient and adaptive to climate risks.”

Transform Climate Challenges into Opportunities with AI

Generative AI is a formidable weapon for insurers in the battle against climate change. It’s been a game changer for data-driven insurers, helping keep customers safe while supporting business growth.

A recent global study by Genpact and analyst firm HFS Research surveyed 550 senior executives across industries. It reveals that 78% of insurance executives agree that gen AI is pushing their organizations toward new and disruptive value creation.

Indeed, gen AI enables insurers to leapfrog traditional and emerging competitors, including insurtechs, by integrating real-time data on weather patterns, sea-level rise, and other climate variables into claims processing. These insights into climate-related risks empower insurers to enhance risk assessments, predict and prepare for weather-related claims, optimize loss mitigation strategies, tailor insurance products, and gain a competitive advantage.

Not surprisingly, then, more than one third of insurance executives cite “competitive advantage” as a top-three benefit of employing gen AI; and a similar proportion highlights “new revenue streams and business models”.

Although 32% also emphasize its role in enhancing “productivity and efficiency,” just half of insurance executives also recognize that a singular focus on productivity could cause their companies to miss out on broader strategic gains.

Boost Claims-Driven Innovation

Claims processes serve as a catalyst for insurance product innovation. The insights insurers get from digging into claims data are like gold dust for refining and pushing forward their insurance offerings. Using AI to analyze claims data enables insurers to spot emerging risks and customer needs, which helps them design products tailored to specific climate-related risks.

That means insurers are not just reacting; they’re proactively shaping tailored insurance solutions. But AI doesn’t stop there. Analysis of claims data can highlight gaps in current coverage. By identifying these gaps, insurers can develop more comprehensive offerings.

AI also plays a crucial role in driving risk modeling for certain innovative solutions. Parametric insurance, for example, is often used in situations where traditional insurance may be inadequate or impractical, such as in regions prone to catastrophic events where assessing individual losses may be challenging or time-consuming. Several companies, including Swiss Re, Munich Re[i], Aon and Zürich offer parametric insurance today, providing quicker payouts and support for those affected by climate change and other emerging risks.

Unlike traditional insurance, which pays out based on assessed damages, parametric insurance pays out a pre-agreed amount as soon as specific conditions are met, like a hurricane hitting a certain strength or heavy rain reaching a specific level. This innovative approach accelerates claims resolution and benefits policyholders.

AI plays a big part in making this happen. With AI-driven risk modeling, parametric insurance can determine exactly when the conditions are met and trigger the payout automatically, eliminating the need for the back-and-forth of traditional claims processing. It’s a game-changer, especially for folks hit hard by extreme weather or natural disasters. AI doesn’t just make a claims adjuster’s job easier; it helps them adapt to new risks and uncertainties in a way that benefits insurers and policyholders.

Improve Predictability of Claims Outcomes

AI significantly improves the predictability of claims outcomes for insurers. By tapping into extensive datasets that transcend traditional boundaries, AI revolutionizes the claims process.

AI uses big data to gain a comprehensive understanding of risks and employs advanced analytical tools for real-time pattern analysis. Machine learning models enhance predictions, while natural language processing extracts insights from unstructured text data. Plus, using synthetic data, insurers can assess risks more effectively and design innovative claims-management strategies, especially when it comes to modeling climate scenarios and rare climactic events.

AI enables the development of more detailed and nuanced risk models based on specific locations, providing profound insights that empower insurers to establish accurate policy pricing guidelines. For example, by analyzing historical imaging data, AI can predict how a hurricane will impact individual properties in Florida. It’s like having a crystal ball for insurance.

With such precise predictions from AI, insurers can avoid or lessen hefty payouts for damages. They can create super-detailed risk models that consider scores of factors, giving insurers a clearer picture of risk. Ultimately, this helps insurers make more informed, data-driven decisions and better protect their customers.

For instance, more granularity helps insurers devise risk-mitigating measures for policyholders, which ultimately reduce claims. A building’s roof type and construction materials impact the severity of damage incurred from hail, heavy snow, or rain. Using advanced models, insurers can provide detailed recommendations about construction materials or irrigation and drainage solutions, tailored to a specific geographic location to help customers lessen damage and risk to their properties and minimize claims costs.

Insurers’ claims operations usually involve a lot of manual work and outdated systems, with little to no integration between carriers and vendors. For example, insurance companies dedicate money and resources to investigating, processing, and settling auto claims through manual and partially automated channels that often don’t connect well. This makes the process cumbersome for adjusters and leaves customers frustrated. AI-driven claims managers can offer seamless virtual auto inspections, creating a better customer experience, while reducing processing time and costs by up to 50%.Another exciting development is that generative AI can foster the use of synthetic data – manufactured information that replicates the statistical properties of real-world data. Synthetic data is used when real data is unavailable or insufficient. Insurance, finance, and healthcare companies use synthetic data to explore or stress test scenarios and hypotheses. For example, many banks are using generative AI to create synthetic data to simulate the impact of rare events, such as stock market crashes.

“Synthetic data is critical to innovation,” says Bipin Chadha, vice president of data science at CSAA Insurance Group.

It is a valuable tool for insurers in claims processing, enabling them to enhance predictive modeling, train machine learning models, preserve privacy, conduct scenario testing, and innovate. With synthetic data, insurers can improve the efficiency, accuracy, and fairness of their claims processing workflows, ultimately benefiting both insurers and policyholders.

Innovate and Automate

MS Amlin recently introduced a new way to handle claims with an AI-powered predictive analytics-based claims triage solution. The solution helps employees evaluate and sort through insurance claims based on factors that determine each claim’s complexity. It gathers additional information from the insurer’s internal databases to give claims adjusters a better understanding of the client and claim. Then it prioritizes, and routes claims for processing, speeding up the settlement process and delighting customers along the way.

Using AI, claims adjusters can quickly analyze data and identify patterns related to climate-related incidents, unlocking faster and more-informed decision-making. This enhanced process helps mitigate the impact of climate change by improving the response to weather-related claims and promoting more resilient recovery strategies.

AdaptAI is transformative for property and casualty insurers facing climate change. By turning climate challenges into strategic opportunities, boosting innovation, and bringing predictability to claims, AI is helping data-driven insurers adapt to the increasing frequency and severity of natural disasters.

Sethi is the global business leader for insurance at Genpact. Saye is global claims leader for insurance for Genpact.