Company: TIPT
Filing Date: 2025-03-03
Form Type: 10-K
Source: 0001393726-25-000028
Chunk: 84

Company: TIPTREE INC.
Filing Date: 2025-03-03
Form: 10-K
Item: Item 1A
Chunk 84
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. dollar relative to the value of the British Pound Sterling, Euro and other currencies in the jurisdictions in which they operate could have a material adverse effect on their business, 

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results of operations, financial condition and cash flows. Their investments in non-U.S.-denominated assets are subject to fluctuations in non-U.S. securities and currency markets, and those markets can be volatile. Non-U.S. currency fluctuations also affect the value of any dividends paid by their non-U.S. subsidiaries to their parent companies in the United States.

Our insurance subsidiaries use AI, machine learning and statistical models to assist their decision-making in key areas, such as underwriting, claims, reserving, and catastrophe risk, but actual results could differ materially from the model outputs and related analyses. 

Our insurance subsidiaries use various modeling techniques, including Stochastic, Bayesian statistics, classification, regression, clustering and other advanced machine learning techniques along with data analytics to analyze and estimate loss trends and other risks associated with their underwriting and claims operations. Our insurance subsidiaries use the modeled outputs and related analyses to assist them in certain decisions involving underwriting, pricing, claims, reserving, reinsurance, and catastrophe risk. As with many technological innovations, AI and machine learning present risks and challenges that could affect their adoption, and therefore our insurance subsidiaries’ business. The assumptions used in deriving modeled outputs and related analyses are subject to uncertainties, model errors and the limitations of historical internal and industry data. In addition, the modeled outputs and related analyses may from time to time contain inaccuracies, which could have a material adverse effect on our insurance subsidiaries’ results of operations, if, based upon these models, they misprice their products, underestimate the frequency and/or severity of loss events, or overestimate the risks they are exposed to. Persistent inaccuracies may adversely impact new business growth and retention of our insurance subsidiaries’ existing clients which could have a material adverse effect on our insurance subsidiaries’ results of operations and financial condition.

Additionally, there are significant risks involved in developing and deploying AI, 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 insurance subsidiaries’ further development, adoption, and use of AI, and may cause them to incur additional research and development costs to resolve such issues. In addition, no assurance can be provided that the usage of such AI will enhance our insurance subsidiaries’ business or assist in being more efficient or profitable. The introduction of