CECL

Our asset liability management (“ALM”) advice engagement begins with a thorough review of our client’s ALM policies, procedures, ALCO minutes, and reporting packages. We then have discussions with members of our client’s senior management team to better understand our client’s goals, objectives and tolerances for interest rate, liquidity and credit risk. Our next step is to review our client’s financial statements for at least the past 10 years to understand the institution’s financial performance. Only after these initials steps do we begin a thorough review of our client’s existing ALM model because we believe we must first have the appropriate context for our analysis. Prior to producing a model for the new reporting period, we obtain inputs from our client’s most recent ALM reporting package and work to successfully replicate the prior results. This step ensures that we have not omitted critical input assumptions in our model review.

We work with our clients to enhance their ALM model input assumptions as we develop the model for the new reporting period. For example, we believe that modeling should specifically address credit risk, and our input assumptions include incidence of expected default (conditional default rate or “CDR”) as well as the severity of the loss to be incurred on a default. Many valuation firms model the credit risk globally by adjusting the allowance for loan and lease losses. Instead, we believe the credit risk should be “built from the ground up” by considering credit indicators such as the credit score of the borrower and the estimated loan-to-value ratio as of the valuation date. We believe that knowing and utilizing these attributes results in a more accurate forecast of voluntary prepayment risk as well. For example, a borrower could have an incentive to refinance, given the interest rate on their loan compared to market interest rates, but they may not have the ability to prepay given their credit score and the estimated loan-to-value ratio as of the valuation date.

We selected the ZM Desk Model because it allows us to model credit risk using CDR and loss severity at the loan or cohort level as appropriate. It also allows us to leverage our existing knowledge and valuation software. For example, we utilize ZM Desk’s direct interfaces with Bloomberg to model “vanilla” investments and with Intex to model investments with complex cash flow waterfalls such as Agency CMOs and Non-Agency MBS.

Wilary Winn believes that the ability to measure risks on an integrated basis by explicitly including expected credit losses leads to superior risk measurement and reporting. We further believe that the ability to predict credit losses at the appropriate level of granularity provides for more reliable Capital Stress Testing, Concentration Risk Analyses, and Real Return Analyses.

“I simply can’t say enough good things about the service we receive from Wilary Winn. From professionalism to accuracy, knowledge of the industry to knowledge of our credit union, they […]”

– Todd Fanning, Interim Chief Executive Officer, GreenState Credit Union – North Liberty, IA