Property insurers face a critical crossroads. On the one hand, an increase in unpredictable, deadly natural catastrophes threatens how insurers currently use historical loss data for future loss projections. This potentially leaves homeowners in catastrophe-prone areas, such as
On the other hand, growing expectations for insurers to
As the nature of risk changes, carriers need to stay nimble in writing policies that can stand the test of time, and AI is a critical component of this agility. It can help insurers get ahead of catastrophes and underwrite better, more accurate policies for homeowners that consider current property conditions and proximity to risk. It can also help improve the claims processes with AI enabling predictive analytics to better understand what types of claims are likely to be filed after an event.
How the industry is responding to AI adoption calls
A recent
AI adoption among property insurers is insufficient in the face of increasingly severe natural catastrophes and high consumer expectations for the speed at which they expect their insurer to respond to claims. For property insurers, the question is no longer about what benefits AI can bring; it's now about how they can accelerate the adoption of these critical technologies.
Here are a few tips for how insurers can accelerate adoption into their organization and leverage AI for higher-impact tasks before rolling it out into the rest of the organization.
Consider avenues for AI adoption
For all the benefits AI tools can bring to insurers, specific questions need to be addressed when determining the best approach for AI. Insurers can build and integrate their own models, which can be expensive and time-consuming and require hiring additional technical staff. Conversely, they can work with established third-party AI vendors.
For example, because climate change is creating unpredictable natural catastrophes, insurers should consider contracting AI vendors with insurance industry experience and scaling their solutions as needed by clients while maintaining model effectiveness. Insurers can ask potential vendors for use cases detailing how their AI models helped others gather required data quickly after a catastrophic event.
Secondly, how the technology is delivered and where the human-machine handshake will take place matters. Insurers can trust the AI model to handle specific tasks such as identifying roof material or conducting property inspections. Still, if the AI gets stuck, the task should be punted to the human. Most importantly, there should be little disruption to day-to-day business operations as the AI tool is being integrated.
Deploy AI for higher-impact tasks first
Deploying AI on essential tasks such as property inspections allows insurers to see higher ROI and better business and customer outcomes. In turn, insurers can dedicate more manpower and resources to making critical organizational decisions.
Once AI can successfully perform property inspections, it can help property insurers create better climate risk mitigation strategies to limit damage to the policyholder's home. However, the onus of action falls to the property owner to take mitigating steps such as trimming treelines so they don't damage roof shingles or securing loose yard equipment that can become projectile-like objects during a storm, such as trampolines and lawn chairs.
Additionally, AI can help insurers assess property damage during and after a natural catastrophe through numerous
Equally important, AI models can be scaled to analyze entire affected ZIP codes without sacrificing accuracy or efficiency. It can also help to underwrite future policies more accurately because the AI models have seen the damage before and can guess how the next weather event might impact the home. This creates a more satisfying insurance experience and gets the policyholder back into their home faster.
AI can even help property insurers keep underwriting new policies in states with higher risks of natural catastrophe exposure, such as California and Florida. This is because the right AI model can derive specific property characteristics most at risk for damage, such as a roof. In turn, this helps insurers collaborate with policyholders in educating and developing protective and mitigation actions they can take to remain insured.
For example, suppose an insurer knows the majority of homes in one Florida ZIP code have a roof type more susceptible to damage by hurricane-force winds. In that case, they know how to create risk mitigation plans and use building material costs to estimate claims payout accurately. It also helps the insurer make recommendations to policyholders during the rebuilding process about what types of roof materials can withstand strong winds to help mitigate future damage.
As natural catastrophes become more severe, property insurers should turn to AI tools to help them adapt and survive. Whether creating tools in-house or contracting a vendor, insurers should develop a checklist of needs before such an undertaking and deploy it to higher-impact tasks first.
