Wilary Winn offers one-time and ongoing ALLL calculations in full accordance with CECL.

Why Choose Us

We believe credit risk should be “built from the ground up” at the loan or cohort level. Wilary Winn’s credit inputs include both the incidence of expected default (conditional default rate or “CDR”) as well as the severity of the loss that will be incurred on a default. We derive our credit loss estimates using discounted cash flow models. Our credit risk inputs are based on the specific attributes of the financial asset such as type of loan, term, fixed or variable rate, combined with predictive credit indicators such as FICO and combined loan-to-value ratio. Our input assumptions are based on the results from the hundreds of engagements we have undertaken since the firm’s founding in 2003.

We consider credit to be the most critical risk that a financial institution faces. Our bottom up credit loss estimates have two primary benefits:

1. Our credit loss estimates are based on the loan attributes and credit indicators lenders use to make loans leading to better integration of lending and financial decision making, including risk-based pricing and Real Return Analyses.
2. Our credit loss estimates can be used to perform capital stress testing. Combining granular credit estimates with interest rate and liquidity risk modeling results in a thorough understanding of the primary balance sheet risks on an integrated basis leading to better allocations of capital. See our Concentration Risk Management and Capital Stress Testing pages for more detail.

What is CECL?

CECL is the acronym for the Current Expected Credit Loss Model. In essence, it requires financial institutions to record estimated life time credit losses for debt instruments, leases, and loan commitments. The big change here is that the probability threshold used to determine the allowance for loan and lease losses is removed and FASB expects losses to be recorded on day one.

CECL requires a financial institution to recognize an allowance for expected credit losses. The expected credit losses are an estimate of the contractual cash flows that are not expected to be collected over the life of the loan. That seemingly simple statement begs further explanation. Let’s begin with the contractual cash flows – the amount of principal and interest a financial institution would receive if the borrower made every payment required under the loan agreement. FASB indicates that contractual cash flows should be adjusted for expected prepayments in addition to the expected losses. On the other hand, the cash flows should not be adjusted for extensions, renewals or modifications unless a troubled debt restructuring (TDR) is reasonably expected.

CECL represents a significant change in the way financial institutions currently estimate credit losses. The standard allows a financial institution to calculate the allowance in a variety of ways including discounted cash flow, loss rates, roll-rates, and probability of default analyses. Whatever methodology is used, the standard requires that the loss estimate be based on current and forecasted economic conditions. When using a discounted cash flow technique, the discount rate to be used is the loan’s effective interest rate – the note rate adjusted for discounts and premiums.

Our Approach

We believe the best analysis technique to be used depends on the type of loan. For example, a financial institution could analyze its commercial real estate loans by re-underwriting its largest loans based on its knowledge of the borrower and current and forecasted economic conditions. It could combine this with a historical migration analysis – how many loans with a risk rating of one migrated to lower ratings over time. Wilary Winn believes the sheer volume of residential real estate and consumer loans in a portfolio preclude loan-by-loan analyses and are best analyzed using statistical techniques. We begin with the contractual cash flows based on the attributes of the loan and adjust them for:

• Voluntary prepayments – which is called the conditional repayment rate – (“CRR”)
• Involuntary prepayments or defaults – which is called the conditional default rate – (“CDR”)
• Loss severity or loss given default – which is the loss that will be incurred – (“loss severity”)

A major advantage of this technique is that it relies on the use of the same credit indicators financial institutions now use to underwrite loans and manage their loan portfolios, including FICO, loan term, and loan-to-value percentage.

The primary disadvantage of this technique is that most institutions have insufficient data to produce statistically valid loss estimates. We overcome this hurdle because we can combine industry-wide data with a financial institution’s specific performance. We do this by using a statistical theory called “creditability”. See our white paper, Implementing CECL, for more details.

Like many, we believe the most challenging part of the standard is the requirement to incorporate forecasted changes in macroeconomic conditions in its estimate. We believe the use of discounted cash flow analyses and updated credit indicators results in the most reliable approach to meeting this requirement, because we can apply assumptions from the bottom up rather than being forced to use a top down assumption. For example, when we are modeling the performance of residential real estate loans, we begin with an updated combined LTV based on a recent AVM. We thus do not have to make an inference regarding the loan’s credit indicators as of the valuation as would be required by other techniques such as vintage analysis – we know what they are. To include short-term changes in housing prices, we utilize forecasts by MSA. Longer term, we incorporate the forecasted change in national housing prices. In this way, we incorporate short-term changes with which we have more certainty with a national forecast that is driven by forecasted economic conditions and historic performance. We use these estimates to change our loss severity estimates. Our models also include a dynamic default vector that is tied to forecasted changes in housing prices. We change our rate of default based on changes to the estimated LTV given normal amortization, curtailments, and changes in housing prices. In this way, we are adjusting our loss estimates based on macroeconomic forecasts.

Wilary Winn believes that while the requirements of the proposed standard can seem daunting, the primary reason to perform the analysis is to be able to better manage credit risk and more efficiently allocate capital.