AI is an insurers new weapon against climate change
Whilst the current buzz surrounding generative artificial intelligence places its focus on text analysis, AI’s ability to interpret visual content is often overlooked. AI and machine learning are able to analyse large quantities of graphics and present them in a digestible manner.
They can therefore assist insurers in studying aerial imagery, detailing thousands of properties. This data can be assimilated and presented in a way which the insurance industry can use to quantify and assess risk, providing invaluable property intelligence.
Training AI Models
As AI becomes more widespread, the models that will become the most useful and widely adopted are those which have access to an exclusive set of data. For example, obtaining aerial imagery of whole continents costs large amounts of money and is not readily accessible.
However, if insurers are able to utilise this data, they can be supplied with the most up-to-date information available on properties in seconds at a reduced fee. Using AI and aerial imagery to assimilate worldwide property databases gives insurers the edge to stay competitive within a fierce market which is currently being governed by climate catastrophes.
AI and Land Surveillance
Property Structure
AI models bring data to life, providing a detailed breakdown of property conditions and analysing 3D-property visuals to reveal hard-to-capture risks. For example, the roof condition of a property can be ascertained in seconds using AI.
As one of the most important underwriting factors an insurer must consider, this information is crucial in allowing companies to offer a fair and competitive price to their customers. This includes the shape, gradient, and material of the roof, as well as any water pooling or staining.
Asking customers the age of their roof is no longer enough. The annual tracking of roof condition is now essential in helping companies to generate actionable insights, negating the need for insurers to rely on incorrect or outdated historical data points. This will allow insurers to reduce the premiums charged to those homeowners who have recently replaced their roof, for example.
Through identifying and monitoring changes in exposure on their renewal book, insurers can also more effectively issue policy regeneration.
AI also has the ability to determine the floor elevation of properties, calculating the number of levels a residence has and the risks associated with the characteristics of any particular building. This is particularly useful if the property is located within a flood risk zone, allowing insurers to calculate future loss more accurately.
Texas and Florida could experience a 50 percent increase in flood exposure by 2050, and states once considered safe from water damage are now starting to appear on FEMA’s flood maps. Thus, this information will become even more important as properties continue to be threatened by rising water levels.
The increasingly unpredictable and damaging weather patterns climate change ushers in demand that insurers have high-quality, accurate, and up-to-date data points regarding a property’s level of risk.
Surrounding Risk Factors
Using AI to extract data points from aerial imagery also allows insurers to go beyond the property itself, providing insights into surrounding risk factors which may affect the coverage offered by the carrier.
This includes property liability hazards such as trampolines and other outdoor features that may increase risk of damage. Insurers can advise residents to take preventive measures and adjust their coverage accordingly.
From high-resolution visuals, AI can also measure property elevation and acreage, monitoring characteristics such as distance from vegetation and other flammable material. This also includes identifying wildfire risk zones, and surrounding structures which pose a threat, like overhanging trees.
Between 2022 and 2023, wildfires accounted for over $3.2 billion in damages across the United States. In addition, according to estimates, severe weather in the US in the first quarter of 2023 will cost insurers between $7bn and $9.5bn after a series of thunderstorm losses from hail, tornadoes, and strong winds.
Thus, risk factors such as these are critical when predicting and preventing losses and it is imperative that insurers have an accurate picture of risk exposure.
Internal Process Acceleration
The ability to harness visual content and transform it into real-time, high-quality data allows for an automated risk assessment. The flexible delivery of data analytics also allows these processes to be easily integrated into the existing workflows of underwriters.
AI and machine learning are helping insurance carriers radically improve traditional underwriting processes. Aerial imagery raises the standard for reliable, cost-efficient property data, and when augmented with analytics based on human expertise, a more reliable risk assessment can be built.
AI can therefore be used to streamline and speed up the underwriting process. For example, insurers can decide which properties they need to send inspectors to, optimising in-person inspections whilst freeing up and effectively distributing valuable resources.
Prediction
With unique data and advanced analytics from aerial imagery, insurers can more confidently assess risk and accurately underwrite throughout the policy life cycle. This will also allow them to improve their current pricing models, offering fair and competitive coverage options within an already saturated market.
AI and machine learning can also allow insurers to undertake catastrophe modelling to estimate the likelihood of damage if a climate catastrophe were to strike. Utilising historical data, the impact of a large-scale extreme weather event, such as a hurricane or flash flood, can be ascertained and factored into the property risk assessment.
Furthermore, population growth in severe weather-prone areas and a lack of adequate architectural disaster-proofing add to the increased cost of natural disasters. Thus, these models can also be used to advise community-centred loss prevention programs as well as government-scale property planning.
Thus, AI and machine learning have the potential to influence planning and property development in order to minimise risk to infrastructure and life.
The Future of AI and the Growing Threat of Climate Change
The rapidly evolving nature of AI and machine learning looks to be a daunting and somewhat dismal picture. However, amongst the noise regarding its uncontrollability and unpredictability, it is important to emphasise the power AI has to help society battle against some of the most brutal challenges humanity has ever seen.
Climate instability is an issue which is not disappearing any time soon, and industries need to ensure they are prepared for things to get worse before they get better. We have already seen the insurance industry struggling to stay afloat within an unpredictable landscape filled with destructive weather patterns.
AI is providing a beacon of hope to an industry which relies on reliable risk predictions. Therefore, insurers must leverage virtual technology to help them rapidly validate essential property characteristics, in turn, allowing them to accurately mitigate risk in an increasingly unstable environment.
