Company: SLDE
Filing Date: 2025-04-25
Form Type: DRS/A
Source: 0000950123-25-003716
Chunk: 83

Company: Slide Insurance Holdings, Inc.
Filing Date: 2025-04-25
Form: DRS/A
Chunk 83
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 change will have on our business.

An overall decline in economic activity could have a material adverse effect on our business, results of operations and financial condition.

The demand for property and casualty insurance generally rises as the overall level of household income increases and
generally falls as household income decreases, affecting premiums, commissions and fees generated by our business. Some new accounts are sourced by referral sources tied to home closing transactions, and major slowdowns in the various housing
markets we serve could impact our ability to generate new business. The economic activity that impacts property and casualty insurance is most closely correlated with employment levels, corporate revenue and asset values.

Our results of operations and financial condition may be adversely affected due to limitations in the analytical models used to assess and predict our exposure to catastrophe losses.

Along with others in the insurance industry, models developed internally and by
third-party vendors that employ various modeling techniques, including Stochastic, Bayesian statistics, classification, regression, clustering and other advanced machine learning techniques, are used along with our own historical data in assessing
property insurance exposure to catastrophe losses. These models assume various conditions and probability scenarios; however, they do not necessarily accurately predict future losses or measure losses currently incurred. As with many technological
innovations, artificial intelligence and machine learning present risks and challenges that could affect their adoption, and therefore our business. Further, the accuracy of such models may be negatively impacted by changing climate conditions.
Catastrophe models use historical information and scientific research about natural events, such as hurricanes and earthquakes, as well as detailed information about our in-force business. This information is used in connection with pricing and risk
management activities. However, since actual catastrophic events vary considerably, there are limitations with respect to its usefulness in predicting losses in any reporting period. Other limitations are evident in significant variations in
estimates between models, material increases and decreases in results due to model changes and refinements of the underlying data elements and actual conditions that are not yet well understood or may not be properly incorporated into the models.
Additionally, there are significant risks involved in developing and deploying artificial intelligence, such as an increase in intellectual property infringement or misappropriation, data privacy, cybersecurity, operational and technological risks,
harmful content, accuracy, bias, toxicity and discrimination, any of which could affect our further development, adoption and use of artificial intelligence, and may cause us to incur additional research and development costs to resolve

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such issues. It is not possible to predict all of the risks related to the use of artificial intelligence, and changes