In recent months, there's been significant coverage of various AI applications in insurance. But one critical area requiring more attention is how insurers can leverage AI—specifically computer vision—to handle both major catastrophic (CAT) and emerging micro-CAT events.
These events are an ongoing challenge for insurers that have no control over when or where they occur. The damage and repercussions from storms like Hurricane
Computer vision
Many insurers leverage high-resolution aerial imagery to monitor properties in their book, or large regions before and after weather events. The imagery is certainly helpful, but AI is necessary to unlock further insights for the insurer at scale. More specifically, this is where computer vision comes into play.
Computer vision is a branch of AI that uses machine learning models to interpret and analyze visual information from imagery. By applying computer vision to aerial imagery, insurers can get specific risk detections around a roof or property's condition and spotlight potential vulnerabilities. A strong system can assess over 100 property attributes—like damaged shingles, rust, or fire paths—in seconds. The ability to spotlight specific attributes that might otherwise go unnoticed enables insurers with a clearer and fuller picture of risk.
This approach is highly practical for making underwriting decisions at the individual property level and can also be applied at scale to larger regions in an insurer's portfolio. In fact, combining imagery with AI significantly improves how insurers assess and respond to severe weather events.
AI for severe weather events
As mentioned earlier, the unpredictability of weather events continues to be a major challenge. However, an end-to-end solution that leverages AI (like computer vision) and aerial imagery can effectively address this challenge both before and after an event, all while accelerating CAT response efforts.
Before an event: Imagery provides a clear view of properties in the insurer's portfolio. Using computer vision models, insurers can detect potential risk factors—like deteriorating roofs or lack of defensible space in wildfire-prone areas—well in advance. This enables them to communicate with policyholders and encourage proactive mitigation. Some of these measures could make the home much more resilient to future damage or reduce the severity of a potential claim.
After an event: After an event, insurers need clear imagery and rapid AI analysis of damage areas. The sooner this information is available, the faster insurers can verify damage, expedite claims, and prioritize responding to the most significant damage, even before the first notice of loss. Most importantly, this information enables insurers to act quickly to support impacted policyholders. The responsibility to provide timely communication and claims support is crucial, especially when homes and communities are devastated. While no one wants these events to occur, insurers—like everyone else—benefit by playing an active role in recovery efforts. Leveraging AI insights not only enhances risk assessment but also empowers insurers to offer better support to their policyholders.
Tackling high-risk areas with AI
As climate risks evolve, more regions across the U.S. are being classified as "less insurable." This isn't just because of major CAT events like hurricanes, but also is due to frequent
AI-driven insights can help insurers in several ways:
- Accurate pricing: With current data from AI detections and geospatial sources, insurers can rate properties more accurately, even in high-risk regions. This precision enables more equitable pricing for policyholders, rewarding those who proactively mitigate risk.
- Proactive risk mitigation: AI provides insights into specific risk factors on a property, like vegetation overgrowth or roof condition. This allows insurers to encourage policyholders to take preventative measures, potentially lowering their premiums.
- Expanded insurability: The detailed view provided by AI means insurers can identify individual properties in high-risk areas that remain insurable. This approach benefits both insurers—by allowing them to write more policies—and policyholders, who might otherwise struggle to find coverage.
The insurance industry has an incredible opportunity to continue protecting assets and guiding policyholders through difficult situations. However, adopting AI tools swiftly and strategically is crucial for enhancing operational capabilities at scale, enabling insurers to respond faster, and empowering customers to actively participate in risk management. With AI, insurers can evolve from reactive responders to proactive partners—predicting, preventing, and delivering better service during the moments that matter most.
