Company: KNSL
Filing Date: 2025-02-21
Form Type: 10-K
Source: 0001669162-25-000010
Chunk: 34

Company: Kinsale Capital Group, Inc.
Filing Date: 2025-02-21
Form: 10-K
Item: Item 1A
Chunk 34
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erves for Unpaid Losses and Loss Adjustment Expenses."

Given the inherent uncertainty of models, the usefulness of such models as a tool to evaluate risk is subject to a high degree of uncertainty that could result in actual losses that are materially different than our estimates, including PMLs. A deviation from our loss estimates may adversely impact, perhaps significantly, our financial results.

Our approach to risk management relies on subjective variables that entail significant uncertainties. For example, we rely heavily on estimates of PMLs for certain events that are generated by computer-run models. In addition, we rely on historical data and scenarios in managing credit and interest rate risks in our investment portfolio. These estimates, models, data and scenarios may not produce accurate predictions and consequently, we could incur losses both in the risks we underwrite and to the value of our investment portfolio.

We use third-party vendor models to provide us with objective risk assessment relating to other risks in our reinsurance portfolio. We use these models to help us control risk accumulation, inform management and other stakeholders of capital requirements and to improve the risk/return profile or minimize the amount of capital required to cover the risks in each of our reinsurance contracts. However, given the inherent uncertainty of modeling techniques and the application of such techniques, these models and databases may not accurately address a variety of matters which might impact certain of our coverages.

Small changes in assumptions, which depend heavily on our judgment and foresight, can have a significant impact on the modeled outputs. For example, catastrophe models that simulate loss estimates based on a set of assumptions are important tools used to estimate our PMLs. These assumptions address a number of factors that impact loss potential including, but not limited to, the characteristics of a given natural catastrophe event; the increase in claim costs resulting from limited supply of labor and materials needed for repairs following a catastrophe event (demand surge); the types, function, location and characteristics of exposed risks; susceptibility of exposed risks to damage from an event with specific characteristics; and the financial and contractual provisions of the (re)insurance contracts that cover losses arising from an event. We run many model simulations in order to understand the impact of these assumptions on a catastrophe’s loss potential. Furthermore, there are risks associated with catastrophe events, which are either poorly represented or not represented at all by catastrophe models. Each modeling assumption or un-modeled risk introduces uncertainty into PML estimates that management must consider. These uncertainties can include, but are not limited to, the following:

•The models do not address all the possible hazard characteristics of