How Generative AI Changes LRO

Lessor's Risk Only (LRO) insurance carriers are benefiting in four key ways from generative AI.

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Everyone involved in selling, buying and using insurance is experiencing a challenging market. Catastrophic weather, inflation and economic conditions have made it difficult for insurers to pay claims and maintain a profit. Some have pulled out of markets, others have announced they’re not taking on new business and many are raising rates. 

There are new risks everywhere, and Lessor’s Risk Only (LRO) insurance, which provides coverage to building owners from claims from tenants, is no different. It can be challenging – for both businesses and insurers – to understand various risks associated with rental office space. Is there a restaurant renting space on the ground floor of the building? Does the foot traffic from a medical office or store raise issues? Is there a manufacturing firm on the premises?

Insurers can’t write insurance for a business if they don’t have a complete view of the risks associated with a commercial space. In fact, for businesses looking to acquire LRO coverage, a policy could end up being more expensive than it needs to be if an insurer is working from limited information. 

Enter generative AI technology. The technology offers real-time information insurers need to fully understand businesses’ risk profiles. The data not only delivers information on occupant risk factors but can be used to streamline other underwriting processes and identify business opportunities. 

Here are four ways generative AI enables insurers to improve LRO underwriting to grow business: 

1. Better insights and increased accuracy: Many insurers say they don’t always have a good understanding of the businesses occupying a building. For instance, the underwriters might be working from historical data that isn’t always up to date. Or the client might only have limited knowledge of the renters in the building and provide the agent incomplete information. 

Using generative AI and large language model technologies, insurers can get real-time insights into tenant occupancy risks. These technologies use publicly available, structured and unstructured data so insurers are working from the most current information.

Generative AI also enables insurers to continue to get insights on a property during the entire policy term. Renting is fluid and can often have significant turnover. Businesses that occupied a space at the beginning of a policy might change over the course of the coverage period. Insurers can now monitor exposures and decide if the policy needs to be updated based on new risks. 

2. Faster decisions on risks: Spending time researching a business or property to understand its risk profile is not only slow but also wasted time if the entity ends up outside of the insurer’s risk appetite. Generative AI can enable insurance organizations to pre-qualify a prospect’s risk profile in a matter of seconds, with just a business name and address. The insurer can then quickly determine next steps.

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3. Prospecting opportunities: When insurers use LRO to underwrite a building, they are presented with a list of current occupants. This could identify other potential prospects within that location. For example, an insurer that targets takeout restaurants might be looking at the risk associated with a takeout pizza restaurant in a strip mall. They might learn from the list of occupants that the strip mall also contains a Chinese takeout restaurant and a Mexican takeout restaurant that they currently do not cover. They can pass this information over to their sales team as a new business lead. Additionally, understanding the business exposures of nearby businesses can help insurers identify potential risks and opportunities in their existing portfolio.

4. Beyond LRO: Insights from generative AI technology can be used across commercial lines business. Better understanding of the risks associated with surrounding businesses enables insurers to more accurately identify if they want to write a particular risk in another line of business. For example, an insurer was evaluating a property for LRO risk and determined that a business in the property was outside its appetite. However, the AI solution identified that the insurer was currently writing commercial policies for three other businesses located in that property. The insurer was able to make adjustments and remove those unwanted risks from their book of business, thereby optimizing their overall portfolio and reducing exposure to potential losses. This highlights the importance of not only identifying new business opportunities but also managing and mitigating risks associated with existing policies based on insights generated by AI technology.

With more current and accurate information, insurers are better able to assess the risks. This means they have more confidence in writing policies and businesses are paying premiums that match their actual risk exposure. With generative AI technologies, insurers can overcome hurdles when underwriting LRO coverage and further use the information to grow their entire commercial book of business.

Chris Schrenk

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Chris Schrenk

Chris Schrenk is chief underwriting officer at NeuralMetrics, a provider of real-time, transparent commercial lines data intelligence for insurance classification and underwriting.

He has extensive experience in commercial insurance and collaborating with leading carriers. His specialization lies in identifying and implementing process improvements that drive automation, enhance underwriting efficiency, improve the accuracy and reduce errors.


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