Company: AXS-PE
Filing Date: 2025-02-26
Form Type: 10-K
Source: 0001214816-25-000056
Chunk: 234

Company: AXIS CAPITAL HOLDINGS LTD
Filing Date: 2025-02-26
Form: 10-K
Item: Item 1A
Chunk 234
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 losses to reinsurer capital and the appetite for certain lines of business. In addition to capacity risk, the remaining capacity may not be on terms we deem appropriate or acceptable or with companies with whom we want to do business. If we are unable to renew our current reinsurance or retrocessional reinsurance or purchase new or replacement coverage on favorable terms or at all, the amount of business we are willing to write may be limited or our protection from losses due to large loss events may be materially reduced. 

We utilize models to assist our decision making in key areas such as underwriting, reserving, investment management, capital assessment, risk management, reinsurance purchasing and the evaluation of our catastrophe risk, and we could be adversely impacted if these models are inadequate or unfit for the purpose for which they are being used.

We employ various modeling techniques (for example, scenarios, predictive, stochastic and/or forecasting) to analyze and estimate exposures and risks associated with our assets and liabilities. We utilize modeled outputs and related analyses to assist us in decision-making, for example, related to underwriting and pricing, reserving, investment, capital assessment, risk management, reinsurance purchasing and the evaluation of our catastrophe risk through estimates of probable maximum losses, or "PMLs". The modeled outputs and related analyses, both from proprietary and third-party models, are subject to various assumptions, professional judgment, uncertainties and the inherent limitations of any statistical analysis, including the use and quality of historical internal and industry data. These models may turn out to be inadequate representations of the underlying subject matter, including as a result of inaccurate inputs or application thereof (whether due to data error, human error or otherwise). Further, to the extent we incorporate automation and machine learning as part of our modeling process, this may lead to heightened risk. Consequently, actual losses from loss events, whether from individual components (for example, wind, flood, earthquake, etc.) or in the aggregate, may differ materially from modeled results. If, based upon these models or other factors, we misprice our products or underestimate the frequency and/or severity of loss events, our results of operations, financial condition or liquidity may be adversely affected. In addition, PMLs are based on results of stochastic models that consider a wide range of possible events, their losses and probabilities. It is important to consider that stochastic events are not an exact representation of actual events. Thus, an actual event does not necessarily resemble one of the stochastic events, and the specific characteristics of the actual event can lead to substantial differences between actual and