CECL Methodology Selection Guide

CECL METHODOLOGY SELECTION GUIDE

Among the many changes and decisions you will need to make to comply with the new CECL
Accounting Standard Update (ASU), perhaps the biggest is deciding which loss method to use to
collectively review your loans and other assets evaluated at amortized cost.
The ASU references static pool, vintage, probability of default, discounted cash flow, and roll rate as
potential methods, but how do you know which method(s) is most appropriate for your institution?
This guide provides three different factors to consider when making your decision:

FEASIBILITY
The first factor is to review your data quality and completeness on a pool level to
determine which methodologies are feasible based on this review. If certain required
data is limited or unavailable it may preclude the use of some methods.
PERFORMANCE
The next factor is to back test each method. This is accomplished by estimating your
allowance on a historical data set and then comparing the results to actual losses for a
comparable period. Back testing should be performed on various data dates and for a
variety of methods and settings.
MANAGEMENT JUDGMENT
The final factor is management’s experience and judgment. Management should
carefully consider the pros and cons of each method, their assumptions and limitations
for use, and the sustainability of preparers using a particular method.

CECL METHODOLOGY SELECTION GUIDE

Example of Methodology Selection
Segmented Pool: Used Auto Loans
CECL Methodology Chosen: Probability of Default
As a result of the analysis of our data in Visible Equity over the last 2.5 years, we have
concluded that the Probability of Default method is the best fit for our “Used Auto” pool of
loans for the following reasons:
• Feasibility - Our institution has sufficient data to use the Probability of Default method. We
have been able to successfully reconcile to the type code all balance information pertaining
to active, charged-off and delinquent loans. We were also able to integrate relevant data on
FICO scores, updated LTVs, and macroeconomic data required to use the method.
• Performance - The results from our back-testing analysis performed on data from June
2016 showed the probability of default method projected losses within X% of actual losses
over the subsequent three (3) years. While other methods produced similar results, we
were pleased with how the model performed.
• Management Judgment - As a management team we weighed the pros and cons of
each method and their assumptions and limitations for use and decided that the probability
of default method is a good, sustainable fit. We assess credit quality using a combination of
credit scores and LTVs, key inputs into Visible Equity’s auto probability of default model, so
this method is a natural fit for our used auto pool.
Given management’s analysis and judgment, we are confident that that Probability of
Default CECL methodology is the best fit for our Used Auto pool, and we believe that it
produces an accurate allowance reserve amount.

STATIC POOL

The strength of this method is in its simplicity. Easily digestible and explainable to
examiners and other stakeholders, the static pool method won’t take much getting used
to. On the other hand, it does require complete, month-end historical balance and charge

off data for at least the term of the class to which you wish to apply it.

Mathematically, this is the simplest method offered by Visible Equity, as it is meant to
resemble a traditional lookback loss rate. Given a class of loans, we wish to obtain a rate
which, when multiplied by the current balance of the class, results in the amount of the

balance expected to not be collected due to credit loss.

ASSUMPTIONS DATA
REQUIREMENTS
• Long historical data availability.
• Consistent age distribution over time. • Month-end balance and charge-off data
going back to contractual terms of the class,
• Historical pool is comparable
to current portfolio. or go back as far as you can to cover
the majority of your losses.
• Accurate charge-off data.

PROS: CONS:

1. Simple calculations. 1. Data availability.
2. Most similar method to 2. Only using small part of history.
current industry standard
3. Loan age assumptions.

VINTAGE

Like the Static Pool CECL methodology, Vintage is a type of loss rate method. It seeks a
rate that represents the percent of a balance expected to not be collected. Vintage

differs from other prominent loss rate methods in its emphasis on origination. Rather than
directly estimating the percent of a remaining balance expected to not be collected,

Vintage targets, for each vintage year, the percent of total originations expected to be
lost in the remaining contractual term.

The vintage method is desirable if you prefer the concept of a loss rate but wish to make
fewer assumptions than are present in the static pool method. Another advantage of

Vintage is that it accounts for loan age. Note the importance of this concept as seasoned
loans generally carry less risk than newer loans, and the level of seasoning in a portfolio

can change dramatically over time.

ASSUMPTIONS REQUDIRAETMAENTS

• Assumes historical data • At least one complete calendar
comparable to current portfolio. year of charge off data and
• Consistent paydowns over time. active balance data.
• File of original loan amount
for all matured loans.

PROS: CONS:

1. Relatively simple calculation. 1. Not recommended for line of credit.
2. Simple Data 2. Can only use original CQI.

3. Accounts for loan age. 3. Based on original loan amounts
instead of current amount.

ADVANCED VINTAGE

Like the Static Pool and Vintage CECL methodologies, Advantage Vintage is a type of loss
rate method. It seeks a rate that represents the percent of a balance expected to not be

collected. In both Advanced Vintage and charge-off ratio methods, a loss rate is
multiplied by the outstanding balance to obtain an allowance (unlike the standard
Vintage method, in which the rate is multiplied by original balance). However, since a
charge-off ratio doesn’t capture the percent of current balance expected to not be
collected over the lifetime of the portfolio as required by CECL, the steps taken to obtain

the loss rate differs between the two methods.

The vintage method is desirable if you prefer the concept of a loss rate but wish to make
fewer assumptions than are present in the static pool method. Another advantage of

Vintage is that it accounts for loan age. Note the importance of this concept as seasoned
loans generally carry less risk than newer loans, and the level of seasoning in a portfolio

ASSUMPTIONS REQUDIRAETMAENTS

• Assumes historical data • At least one complete calendar
comparable to current portfolio. year of charge-off data and
active balance data.

PROS: CONS:

1. Based on current balances. 1. Complex calculation.
2. May use current or original CQI. 2. Less transparent
3. Relatively simple/familiar calculation.
(can’t drill down to loan list).
Account for loan age.
Works well for line of credit.

Uses entire data history.

PROBABILITY OF DEFAULT

The probability of default (PD) method takes a fundamentally different approach than
vintage and static pool. Rather than producing a rate to multiply by current balance, PD
leverages loan and economic factors to produce monthly projections of credit loss. This
method is truly a loan-level method, as it turns loan-level and economic characteristics

into monthly probabilities of default.
The main appeal of this method is its ability to seamlessly incorporate loan quality and
macroeconomic factors into your loss estimates. Additionally, because of Visible Equity’s
robust database, the discrete time survival models are fit with an accuracy that usually
isn’t possible with data from a single institution. Another benefit is the leniency in terms of

data requirements.

ASSUMPTIONS REQUDIRAETMAENTS

• Loans with a given set of risk characteristics • Complete reporting date data.
will behave similarly to industry loans • Reconciled reporting data.

with the same set of risk characteristics.
• Loans that do not have required data in class

are represented by those that do.
• You must assume a cost-to-sell value.

• Assume regular payment patterns.

PROS: CONS:

1. Only need current data. 1. New and less familiar.
2. Q&E and forecast adjustments

seamlessly integrated.
3. Given that it’s based on industry data,

you’ll be on par with industry data.
4. Reacts quickly to changes in portfolio.

DISCOUNTED CASH FLOW WITH
PROBABILITY OF DEFAULT

DCF-PD uses many of the same components as PD but aggregates them under a
different framework. Monthly probabilities of default and prepay are produced with the
discrete-time survival models, but rather than applying them with LGD, they are used to

estimate future monthly cash flows.
The strengths of DCF-PD align closely with those of the PD method. Loan quality and
economic factors are naturally integrated into your allowance calculations, and only

current loan-level data is required.

ASSUMPTIONS REQUDIRAETMAENTS

• Loans with a given set of risk characteristics • Complete reporting date data.
will behave similarly to industry loans • Reconciled reporting data.

with the same set of risk characteristics.
• Loans that do not have required data in class

are represented by those that do.
• You must assume a cost-to-sell value.

• Assume regular payment patterns.

PROS: CONS:

1. Only need current data. 1. New and less familiar.
2. Q&E and forecast adjustments

seamlessly integrated.
3. Given that it’s based on industry data,

you’ll be on par with industry data.
4. Reacts quickly to changes in portfolio.