Company: KEY-PI
Filing Date: 2025-02-21
Form Type: 10-K
Source: 0000091576-25-000038
Chunk: 276

Company: KEYCORP /NEW/
Filing Date: 2025-02-21
Form: 10-K
Item: Item 7
Chunk 276
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 management’s estimate of all expected credit losses over the expected contractual life of our existing loan portfolio. Determining the appropriateness of the allowance is complex and requires judgment by management about the effect of matters that are inherently uncertain. These critical estimates include significant use of our own historical data and complex methods to interpret them. We have an ongoing process to evaluate and enhance the quality, quantity, and timeliness of our data and interpretation methods used in the determination of these allowances. These evaluations are inherently subjective, as they require material estimates and may be susceptible to significant change, and include, among others:

•PD,

•LGD,

•Outstanding balance of the loan,

•Movement through delinquency stages,

•Amounts and timing of expected future cash flows,

•Value of collateral, which may be obtained from third parties,

•Economic forecasts which are obtained from a third party provider, and

•Qualitative factors, such as changes in current economic conditions, that may not be reflected in modeled results.

As described in our accounting policy related to the ALLL in Note 1 (“Summary of Significant Accounting Policies”) of this report under the heading “Allowance for Loan and Lease Losses," we employ a disciplined process and methodology to establish our ALLL, which has three main components: (i) asset specific / individual loan reserves; (ii) quantitative (formulaic or pooled) reserves; and (iii) qualitative (judgmental) reserves. 

We use a non-DCF factor-based approach to estimate expected credit losses that include component PD/LGD/EAD models as well as less complex estimation methods for smaller loan portfolios. Probability of default models estimate the likelihood a borrower will cease making payments as agreed. These models use observed loan-level information and projected paths of macroeconomic variables. Borrower credit attributes including FICO scores of consumers and internally assigned risk ratings for commercial borrowers are significant inputs to the models. Consumer FICO scores are refreshed quarterly and commercial risk ratings are updated annually with select borrowers updated more frequently. The macroeconomic trends that have a significant impact on the probability of default vary by portfolio segment. Exposure at default models estimate the loan balance at the time of default. We use an amortization based formulaic approach to estimate account level EAD for all term loans. We use portfolio specific methods in each of our revolving product portfolios. LGD models estimate the loss we will suffer once a loan is in default. Account level inputs to LGD models include collateral attributes, such as loan to value.

If we observe limitations in the