How aerial imagery and AI can help mitigate disaster risk

By Izik Lavy

Climate disasters have increased in recent decades, leaving economic losses from natural disasters at $313 billion in 2022. Approximately 42% of these losses was covered by insurance, leaving a significant protection gap.

An insurance agent’s role in assessing risk is critical. Agents must obtain and submit the right properties to the right carrier, a task in which data plays a pivotal role. If an insurer isn't selective about the properties they offer coverage on, the insurer could pay out more money in claims and expenses than they collect in premiums.

An agent who can provide reliable data on the properties a carrier is insuring can help carriers make balanced decisions about who to insure. This is why we are seeing carriers choosing not to cover properties in certain locations based on generalized perceptions of risk in those areas. In those locations, no matter what properties are submitted, carriers will immediately disqualify them. However, when equipped with enough high-quality data points, agents can boost sales by targeting properties that other insurance companies are not considering, winning the sale with zero competition.

The climate emergency requires that carriers improve their methods of assessing risk accurately, allowing them to offer further protection to those customers who aren’t too risky to insure. However, the data provided by agents is often inaccurate and outdated due to poor collection methods undertaken in the past. As climate change continues to bring unpredictable weather patterns, this obsolete data source has made an agent's method of determining risk redundant. Old-fashioned methods and data sources often hide the true extent of risk to both the insurer and the insured.

Integrating artificial intelligence and aerial imagery can provide rich, up-to-date property data. This data will allow agents to provide more accurate data to underwriters. Providing good quality data is in an agent’s interest, especially in the case of commercial, wholesale and specialty insurance where they can bind a greater number of high premiums by delivering high quality data to underwriters. In these cases they gain credibility and build trust so underwriters are comfortable approving their submission more quickly, meaning they can ultimately close more deals.

For an agent, better quality data will also provide an opportunity to identify properties with a risk lower than what the carrier believes. If the agent can persuade a carrier to insure a property that was previously thought to be uninsurable, they gain market share that their competitors didn’t want to touch previously. For example, if an agent has access to data about the amount of vegetation near houses in a wildfire risk zone, they may be able to offer insurance to households that no other agency is working on if they recognize that the risk is lower than previously thought. Or in a flood risk zone, they can collect information about first floor elevation to ascertain what the actual damage risk from a flood would be.

Aerial imagery: A bird’s-eye solution

To tackle the carriers’ challenge of losses due to climate change and gain their support, it is essential for insurance agents to accurately assess risk. The use of machine learning and aerial imagery can provide an innovative solution. By using data collated by AI, agents can access relevant and comprehensive property details, helping them avoid bad risk submissions.

Loss prediction plays a critical role in the rate development process. For insurers to develop underwriting rates that reflect the risks they are insuring, they must estimate losses accurately. Among the largest factors influencing underwriting estimates is the roof of a property. Therefore, agents must present accurate and up-to-date data to underwriters regarding its condition to offer a fair and competitive quote to the customer. A partnership between AI and aerial visuals can determine the shape, gradient and material of a roof. This enables a comprehensive assessment of the roof’s overall state and structural properties. It is essential to ensure that the assessment process is accurately informed in order to guarantee complete coverage for any damage the client may suffer.

Access to precise data is crucial for agents to pose valid risk submissions to underwriters. Key underwriting attributes ultimately affect the profitability of the claim after a disaster. As a result, machine learning and aerial imagery provide an effective way of assessing risk. This innovative approach can help agents provide the best service possible to their customers while maintaining strong relationships with carriers.

Time is an essential resource

The current process of agents traveling extensively to survey properties is not only tedious, but also consumes time, money and resources. Data generated using aerial imagery offers a solution to this problem. With such data, agents can quickly wrap all the required data regarding a property, and even extract information to support their case that was not available until today. For example, retrieving data regarding the roof age which is based on historical imagery can qualify a property in the eyes of an underwriter.

This approach allows agents to focus on specific properties and employ AI to extract key data points such as the roof’s condition and material, as well as geographical distance from potential hazards such as vegetation.

By harnessing the power of AI and machine learning, agents can accelerate the assessment and submission process, resulting in significant cost savings and improved efficiency. In addition, agents can fill in Association for Cooperative Operations Research and Development forms accurately and with high quality data. This enables standardization and consistency between carriers, brokers and agents.

Gaining customer trust

Agents must be confident they can approach the customer with accurate data on their property, asserting that the carrier they represent is the best provider option. Within a competitive industry, an agent must be an expert in their field, making the customer experience targeted and personable. Therefore, agents need access to as many data points as possible before the initial consultation to demonstrate a breadth of knowledge on the homeowner’s property.

Using the data collected by AI and aerial imagery, agents can also advise customers on how to reduce risk and lower their premiums. This data allows agents to reach out to new clients with high risk attributes such as excessive vegetation in wildfire danger zones, giving them tips about how to reduce the risk associated with their property. This builds a relationship between agent and customer, making policy a two-way process that aims to benefit the insurer and the customer. Buyers can therefore be offered a competitively priced premium and the insurer can confidently assume the risks described in the policy.

Climate change versus insurance agents

With extreme weather on the rise, so too are global property insurance premiums, an increase of 5.3% annually through 2040, according to the Swiss Re Institute. Climate change is causing many homeowners to re-evaluate their coverage or find new policies, and in some cases, pay more for the same coverage.

Customers are also being dropped by their insurance providers, making it difficult to find coverage in areas classed as high risk. Notoriously vulnerable areas, however, are not the only victims of natural disasters. Climate change is also bringing damage to unexpected places. For example, the U.S. has been battered recently by winter storms bringing blizzards into the beginning of spring.

With the latest advances in AI and aerial imagery, agents have an opportunity to provide accurate, objective information upon which underwriters can mitigate risk, giving them a pivotal role in identifying the right properties for carriers.

Izik Lavy is CEO at GeoX. He may be contacted at [email protected].