Articles - AI machine learning and aerial imagery

As climate change forces actuaries to make radical new risk calculations, how can the insurance industry protect itself. Climate change is sweeping the globe, bringing scorching heat waves and long droughts to tropical cyclones and torrential rains. The global threat posed by climate change was made stark most recently by the hazardous air quality levels that arose from Canada’s wildfires, engulfing millions of Canadians as well as those in major US east coast cities.

 By Izik Lavy, CEO of GeoX
 Wildfires have also recently raged in California, south-east Australia, and parts of the Mediterranean, due to higher temperatures and longer dry seasons. Global losses from wildfires between 2018 and 2022 reached $69bn, with insurers paying out $39bn in claims. Climate change is making it particularly difficult for actuaries to assess and manage financial risk, leaving insurance companies unable to stay afloat in an already competitive industry.

 AI, Machine Learning, and Artificial Intelligence

 Risk Assessment
 The ability for actuaries to complete accurate assessments relies on their capacity to survey and quantify the potential financial risks associated with insuring individual properties. However, what is needed is a better picture. More up-to-date, accurate data is required in order to improve risk assessment and act quickly. Aerial imagery can be used to instantly and accurately map properties on a 3D scale. Using this imagery to generate rich data sets, carriers are able to determine what is the real risk associated with a single property and cope with new circumstances caused by climate change. Moreover, underwriters can decide whether there is cause for actual inspection, distributing resources effectively whilst saving time and money.

 Machine learning can be applied to the visual data gained from aerial imagery, identifying and drawing out specific elements of a property which may ultimately increase the likelihood or size of a pay-out. For example, a property’s roof condition is one example of a critical underwriting attribute which insurers must consider. Roofs are usually the first element of a property to be damaged during extreme weather, bearing the brunt of the destruction. Surrounding risk factors, such as overhanging trees, increase the probability of damage. This also includes the distance of a property to vegetation zones which may pose a wildfire risk. These types of criteria can be used to evaluate a property’s likelihood of suffering damage during severe weather conditions, allowing actuaries to formulate its risk profile and advise on premium pricing.

 AI can provide actuaries with these kinds of rich data points on a mass-scale in seconds, allowing them to ascertain the risk undertaken by the insurer for each individual property. By accurately assessing and quantifying risks through the application of aerial imagery, machine learning, and AI, actuaries enable insurers to provide affordable coverage to policyholders while maintaining a profitable business model. Furthermore, with this data actuaries can discover properties whose risk has been overestimated by other insurance companies. By suggesting insurance coverage for properties which have been overlooked by other insurers, whilst avoiding those which pose too high a risk, they improve sales without increasing risk exposure. On the flipside, they can avoid high-risk properties whose risk is underestimated by competitors. Overall, this would reduce financial losses and adjust book risk appetite.

 Pricing and Underwriting
 Actuaries assist insurance companies by determining the level of risk associated with each individual property, advising companies on the appropriate premiums to charge for property insurance policies. Not only is this determined by the risk factors discussed above, but also by factors such as property history, market conditions, and geographical location.

 AI also has the ability to provide actuaries with historical information around previous roof replacements. For example, if an AI model can flag that the roof of a property has recently been replaced, this will lower insurance premiums for the homeowner. By quantifying the risk accurately, actuaries ensure that the premiums charged align with the expected costs of potential claims as well as the profitability goals of the insurance company.

 Catastrophe Modeling
 In order to estimate the likelihood and magnitude of future events, actuaries must have access to up-to-date and high quality data points. Actuaries can also use AI, machine learning, and aerial imagery to develop and utilize catastrophe models to assess the potential impact of large-scale events like hurricanes, earthquakes, or wildfires on property insurance portfolios. These models incorporate historical data in order to estimate the likelihood and severity of catastrophic events.

 In addition to this, after a natural catastrophe has struck, the only way to measure damage in large areas is by using AI to process aerial imagery from before and after disaster. Tracking these changes will also help professionals to understand what properties were damaged and the extent of this damage, allowing them to incorporate these key insights into their models. In this way, information surrounding a property’s history of flooding or wildfire exposure, for example, can be utilized to predict future damage.

 By understanding and quantifying these risks, insurers can appropriately manage their exposure and set appropriate premiums.

 Working closely with other professionals, such as underwriters and risk managers, actuaries develop strategies for risk mitigation. They provide insights and recommendations on risk control measures, loss prevention programs, and policy terms and conditions that can reduce the likelihood and impact of property-related losses. This information can also be taken into account when developers build new properties, adapting them in such a way to minimize damage during environmental upheaval.

 The Future of Insurance
 Aerial imagery, machine learning, and AI can assist actuaries in their mission to help insurance companies make informed decisions about property underwriting and risk management.

 Florida’s property market is an example of the insurance market in crisis, as insurers grapple with billions in losses from recent natural disasters such as extreme flooding. Similarly, four out of the five most costly wildfires in the past decade have been in California. As a result, State Farm, one of the country’s largest insurance companies, announced that it would stop selling coverage to California homeowners — not only in wildfire zones but across the state. This is a trend which we can expect to see across the globe as climate change makes it more difficult for companies to profitably insure properties.

 Actuaries ensure the long-term financial stability of an insurance company, but climate change is making this more difficult. Historical weather trends are no longer reliable, and without accurate data insights, they cannot confidently assess risk. Actuaries can more accurately understand the implications of different risk scenarios with high-quality data points, helping insurers make key decisions.

 In this way, AI and machine learning applied to high definition aerial imagery can protect the insurance market from the threat of collapse.

Back to Index

Similar News to this Story

Aiming for calm seas in our market reforms
The size and scale of the UK financial sector is worth reflecting on. It employs more than 2.5 million people and produced £278bn of economic output
Key innovations and changes impacting insurers in 2024
The speakers explore the impact of AI in the insurance industry, the industry's role in social issues, climate change, closing the global protect
Murder on the LPI floor
DB schemes, by and large, are in a far healthier position than they have been for over a decade. This is good news, but it also changes the challenges

Site Search

Exact   Any  

Latest Actuarial Jobs

Actuarial Login

 Jobseeker    Client
Reminder Logon

APA Sponsors

Actuarial Jobs & News Feeds

Jobs RSS News RSS


Be the first to contribute to our definitive actuarial reference forum. Built by actuaries for actuaries.