How AI can support the insurance hurricane response
Weather modeling and forecasting increasingly make use of AI to speed up computations and to increase accuracies.
Artificial intelligence (AI) is an umbrella term covering many diverse mathematical methods for learning patterns, from empirical data and then utilizing these to bring out useful information or to automate manual processes.
Focusing on the insurance industry in hurricane season, let’s have a look at some concrete value-add use cases that AI can provide — both the generative and traditional types of AI.
Weather modeling and forecasting increasingly make use of AI to speed up computations and to increase accuracies as well as to get more detailed local information. When it comes to forecasting what areas will be impacted, where the wind and water damages will occur and how bad they will be, AI is at the center of that forecast. This gives vital information to residents and businesses by providing advance notice they can use to prepare and vacate the premises.
Based on satellite and other data, AI can analyze the progress of hurricanes in real-time. Combining this information with the forecasting capabilities and telecommunications, AI is able to warn the population affected in a targeted and timely manner. Introducing mapping and traffic modeling as well, AI can suggest the best routes to vacate the area with the least congestion and fastest time to reaching a safe area, which is not necessarily far away.
Overlaying real estate data with civic data, AI can provide real-time forecasts to insurers on the likely damage incurred. In advance of an event, AI provides accurate local and granular assessments of risk for properties applying for insurance coverage and so allows for dynamic pricing based on real risk exposure.
Computer vision can be used to interpret photos of buildings, cars and other infrastructure. Doing this prior to a hurricane can determine both the value of the asset as well as its robustness to hurricane damage. After a hurricane, this capability can be used to determine the extent of damage as well as the cost to restore the asset.
Insurance companies rely on actuarial tables in computing insurance premiums. Such tables contain the risk for particular kinds of assets under certain conditions. Keeping these up-to-date is an insurance company’s secret sauce and remains a significant on-going undertaking. AI methods can help automate this process and to keep this information in a more detailed and more local variety allowing a more accurate assessment of risk exposure.
Moving to the generative side of AI, large language models (LLMs) can help claimants in entering their claim information and communicating with them regarding the process and status of their claim. Traditional AI methods mentioned above can be used to partially automate claim processing by analyzing data, particularly images of damaged assets. Any documents uploaded as images can also be interpreted by traditional AI methods and made available to generative AI methods for intelligent processing.
Combining claim information with contracts and policies of the insurance provider using generative AI can provide an accurate first opinion on whether certain damages are covered by the insurance, what provisions apply, or what information may still be missing.
Such approaches using LLMs can be used also to provide pre-sales and post-sales advice and counsel to customers of insurance to assure them of what coverage they are buying. Often, some simple physical measures to improve the property will serve to lower a customer’s risk class. Identifying such measures is normally too expensive for an insurance company but using AI methods this is within reach and could provide a win-win application for consumers, communities, and insurance providers alike.
Advice does not need to be long-term and structural. Insurance companies might also provide short-term advice to customers immediately prior to a hurricane on how to prepare for it. Simple measures in home preparation could significantly reduce the damage done. LLMs are capable of supplying this information in a timely manner and at scale to a large population but also personalized to the individual needs of particular customers.
Traditional AI and its ability to handle numerical data well has been around for many years and its main benefits are for the insurance companies internally. Generative AI with its facility to ingest and produce language, can now be used productively to communicate with customers in a personalized way at scale. This is something new that was, until now, not possible. Opening a true two-way channel of communication enables a closer cooperation between insurer and customer as both parties want there to be the least amount of damage.
In closing, AI can provide benefits to both insurance companies and customers of insurance, especially with a view to natural disasters like hurricanes. The benefits are centered around the accurate forecasting of damage and a smooth two-way communication largely automated and personalized using various AI methods to solve the very practical and sometimes urgent problems encountered in emergency situations.
Patrick Bangert is senior vice president of data, analytics and AI at Searce. This article is published with the author’s permission and may not be reproduced.
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