AI & aerial imagery
At Nearmap, one of our core uses of AI involves analyzing aerial imagery to assess property risk — also known as computer vision. We’re building an accurate, continually updated model of the physical world to identify risks, trends, and changes.
For instance, Nearmap AI can already process and interpret millions of square kilometers of aerial data, identifying details that are crucial for accurate risk assessment. As another example, Betterview, our comprehensive insurance platform, can detect conditions of buildings, risk factors like solar panels or swimming pools, and environmental hazards, and then either flag or automate risk profiles. The result is insurers get a true view of risk and reduce the guesswork and generalizations of traditional insurance assessments.
GenAI also plays a role in enriching our technology. For example, a large language model chatbot is leveraged in the Betterview platform, where an insurer can query about the risk profile of a property and receive an answer in seconds. While it’s an exciting trend, the focus on rock-solid AI Systems means outcomes are more important than the precise technique used.
So, GenAI has a place, but there’s much more to making real-world AI. Nearmap AI is a holistic system that goes from pixels to risk models, and a complex, carefully designed set of algorithms and systems between. It can assist with:
Detailed risk detection: AI systems can be trained to detect various conditions and features of properties that are critical for assessing their risk levels. For example, AI can automatically identify building characteristics like roof integrity, age, and materials used — which can impact the vulnerability of the property. Environmental hazard analysis: AI algorithms extend to detecting environmental hazards that could impact properties. This includes identifying overgrown vegetation that might pose fire risks or unstable land that could lead to subsidence. These insights are crucial in regions prone to specific types of natural disasters, allowing insurers to evaluate potential risks more comprehensively. Impact on insurance policies: Having a thorough understanding of property risks helps insurers to customize policies that better reflect the actual conditions and potential risks associated with each property. This ability to tailor policies based on detailed, property-specific risk assessments significantly reduces the reliance on broad actuarial tables and guesswork. Improve underwriting precision: The use of AI systems in analyzing aerial imagery leads to more precise underwriting. Insurers can adjust their policies and pricing to more accurately match the risk level of each property, improving their risk management strategies. This level of precision not only benefits insurers by reducing unexpected payouts but also benefits policyholders by not overcharging them for lower-risk properties.
As GenAI, and AI Systems more broadly, continue to transform the insurance industry, what does the future hold for this technology? With its potential for continuous improvement and innovation, we can expect even more advancements in risk assessment, policy customization, and decision-making processes.