An Overview of the Current Expected
Credit Loss Model (CECL) and
Supervisory Expectations
(Steve Merriett, Joanne Wakim, Shuchi Satwah)
Friday, October 30, 2015
1pm – 2:30pm ET
12pm – 1:30pm CT
11am – 12:30pm MT
10am – 11:30am PT
9am – 10:30am AT
8am – 9:30am HT
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Welcome Everyone
• Logistics
– Call-in number: 888-625-5230 (code: 92868194#)
– https://www.webcaster4.com/Webcast/Page/584/11269
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• How we’ll take questions
– Use the chat feature in the webinar (Ask Question button on bottom of
screen)
– Email your question to: [email protected]
© 2015 Federal Reserve Bank of St. Louis
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Our Presenters and Host Today
Julie Stackhouse
Federal Reserve Bank
of St. Louis
Steve Merriett
Federal Reserve
Board of Governors
Joanne Wakim
Shuchi Satwah
Federal Reserve
Board of Governors
Federal Reserve
Board of Governors
© 2015 Federal Reserve Bank of St. Louis
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Purpose Statement
• After this session, you will be able to:
– Recognize the key elements of the proposed standard
– Understand the Fed’s perspective on CECL
– View Vintage Analysis as one way to collect and analyze historical loss data
© 2015 Federal Reserve Bank of St. Louis
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Agenda
• Recap
–
–
–
–
Likely timeline of the proposed standard
Key elements of CECL
Measurement of expected credit losses
Supervisory approach and data requirements
• Analysis of historical loss data under CECL
– Annual loss rates
– Vintage data
• Resources
• Your questions
© 2015 Federal Reserve Bank of St. Louis
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Likely Timeline of the Proposed Standard
• Finalized standard expected to be issued by the Financial Accounting
Standards Board (FASB) in first quarter 2016
• Possible implementation date of January 1, 2018, though most likely to
be later
© 2015 Federal Reserve Bank of St. Louis
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Key Elements of the Proposed Standard
• Allowances are to be based on CECL model
– CECL is applicable to loans and debt instruments held at amortized cost, as
well as receivables, lease receivables, and loan commitments
• Expected credit losses are defined in the exposure draft as “current
estimate of all contractual cash flows not expected to be collected.”
– No triggers, no thresholds
• Quicker recognition of losses is an expected outcome
– Changes in allowance balances reflect changes in credit quality and flow
through bank earnings
© 2015 Federal Reserve Bank of St. Louis
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Measurement of Expected Credit Losses
• A current estimate of all contractual cash flows not expected to be
collected should incorporate:
– Internally and externally available information
– Information about past events, current conditions, and reasonable and
supportable forecasts
– Quantitative and qualitative factors specific to borrowers and the economic
environment, including underwriting standards
Unadjusted
historical
lifetime loss
experience
Adjustments
for past
events and
current
conditions
Adjustments
for
reasonable
and
supportable
forecasts
© 2015 Federal Reserve Bank of St. Louis
Estimate of
expected
credit losses
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Measurement of Expected Credit Losses
(continued)
• Choice of methods include:
–
–
–
–
–
Loss-rate methods
Probability of default methods
Discounted cash flow methods
Roll-rate methods
Provision matrix method using loss factors
• Any reasonable approach may be used, provided it reflects that some
risk of default, however small, always exists and that zero allowance
for loan and lease losses would be rare.
• Entities should leverage current internal credit risk management
approach and systems to measure expected credit loss.
© 2015 Federal Reserve Bank of St. Louis
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Supervisory Approach
• Institutions are encouraged to:
– Become familiar with the proposed changes
– Involve all relevant business lines in preparation for the implementation of
the CECL model
– Discuss the proposed CECL model with industry peers and external auditors
– Begin identifying and collecting actual loss data required for the
implementation of the CECL model
© 2015 Federal Reserve Bank of St. Louis
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Data Collection and Analysis
• Many banks currently use a historical loss rate method to estimate
allowances and have processes in place to collect and analyze annual
charge-off data
• Existing data collection processes may require changes under the CECL
model
• We will use an example to illustrate:
– Collection and analysis of annual loss data
– Collection and analysis of lifetime loss data based upon vintages
© 2015 Federal Reserve Bank of St. Louis
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Example
• Assumptions
– Entity B, a lending institution, provides financing to farmers
– The four-year amortizing loans are secured by the farm equipment
purchased by the borrowers with proceeds from the loan
– Each loan is for $100 at 5 percent interest per annum, and the bank makes
1,000 loans the first year
– The bank begins making these loans in 2007, and it experiences a growth
rate of 10 percent per annum in the number of loans it makes each year
© 2015 Federal Reserve Bank of St. Louis
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Originations
By year-end 2015, Entity B would have originated loans as shown in the
table below:
Year of
Origination
No. of New
New
Loans
Origination
2007
1000
$100,000
2008
1100
110,000
2009
1210
121,000
2010
1330
133,000
2011
1460
146,000
2012
1610
161,000
2013
1770
177,000
2014
1950
195,000
2015
2150
215,000
© 2015 Federal Reserve Bank of St. Louis
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Analyzing Annual Loss Data
By year-end 2015, Entity B would have collected the following data by
tracking new originations, principal repayments, charge-offs, and
recoveries:
EOY
Reporting
Beginning
Balance
New
Outstanding Originations
[a]
[b]
Principal
Repaid
ChargeOffs
[c]
[d]
2007
$0
$100,000
$0
2008
100,000
110,000
2009
186,415
2010
Ending
Balance
Outstanding
[e]= [a]+[b][c]-[d]
Annual
Gross
Charge-Off
Rate*
Recoveries
[f]=[d]/[a]
[g]
Annual Net
Charge-Off
Rate
[h] = [[d][g]]/[a]
$0
$100,000
23,085
500
186,415
0.50%
-
0.50%
121,000
49,209
1,929
256,277
1.03%
200
0.93%
256,277
133,000
78,522
3,221
307,534
1.26%
772
0.96%
2011
307,534
146,000
111,540
4,184
337,810
1.36%
1,289
0.94%
2012
337,810
161,000
122,396
4,532
371,882
1.34%
1,674
0.85%
2013
371,882
177,000
134,836
5,389
408,658
1.45%
1,813
0.96%
2014
408,658
195,000
147,703
6,782
449,172
1.66%
2,155
1.13%
2015
449,172
215,000
161,984
7,467
494,721
1.66%
2,713
1.06%
*Calculated as Charge-Offs divided by Beginning Balance Outstanding
© 2015 Federal Reserve Bank of St. Louis
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Realigning Charge-Off Data
If the entity were to track the components of annual charge-offs based on
the age of the loans and origination year, it could better discern trends.
EOY
Reporting
2007
New
Origination
[a]
1 Year Old
2 Year
Old
3 Year
Old
4 Year
Old
[b]
[c]
[d]
[e]
$100,000
Annual
ChargeOffs
[d]=sum [b]
to [d]
$0
2008
110,000
500
500
2009
121,000
700
1,229
2010
133,000
500
1,306
1,416
2011
146,000
800
1,306
1,783
295
4,184
2012
161,000
700
1,459
1,835
537
4,532
2013
177,000
1,100
1,920
1,993
376
5,389
2014
195,000
1,400
2,381
2,465
537
6,782
2015
215,000
1,400
2,458
2,884
725
7,467
1,929
© 2015 Federal Reserve Bank of St. Louis
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Collecting Vintage Data
If the entity were to recalculate the loss rates by using the charge-off
amounts and the amount of new originations specific to that vintage year,
it could calculate cumulative (i.e., lifetime) loss rates.
Year of
Origination
Year 1*
[a]
Year 2*
[b]
Year 3*
[c]
Year 4*
Cum ulative
Loss Rate
[d]
[e] = sum [a]
to [d]
2007
0.50%
1.23%
1.42%
0.30%
3.44%
2008
0.64%
1.19%
1.62%
0.49%
3.93%
2009
0.41%
1.08%
1.52%
0.31%
3.32%
2010
0.60%
1.10%
1.50%
0.40%
3.60%
2011
0.48%
1.32%
1.69%
0.50%
3.98%
2012
0.68%
1.48%
1.79%
2013
0.79%
1.39%
2014
0.72%
0.40%
3.65%
2015
Average
0.60%
1.25%
1.59%
*Calculated as Charge-Offs divided by Original Amount Outstanding.
© 2015 Federal Reserve Bank of St. Louis
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Analyzing Vintage Data
If the entity were to collect loss rates by vintage, it could better discern
trends.
© 2015 Federal Reserve Bank of St. Louis
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Analyzing Vintage Data
(continued)
•
•
•
•
•
The majority of losses
emerge in years 2 and 3.
Losses have been
worsening since 2009.
2012 actual loss rate is
almost equal to the rate
on 2011 loans.
There is an oversupply
of used farm equipment.
Severe weather in
recent years has
increased the cost of
crop insurance, and this
trend is expected to
continue.
Year of
Origination
Year 1*
Year 2*
Year 3*
Cum ulative
Year 4* Loss Rate Projected
2007
0.50%
1.23%
1.42%
0.30%
3.44%
2008
0.64%
1.19%
1.62%
0.49%
3.93%
2009
0.41%
1.08%
1.52%
0.31%
3.32%
2010
0.60%
1.10%
1.50%
0.40%
3.60%
2011
0.48%
1.32%
1.69%
0.50%
3.98%
2012
0.68%
1.48%
1.79%
2013
0.79%
1.39%
2014
0.72%
4.60%
4.80%
5.00%
2015
Average
5.10%
0.60%
1.25%
1.59%
0.40%
3.65%
4.88%
*Calculated as Charge-Offs divided by Original Amount Outstanding.
© 2015 Federal Reserve Bank of St. Louis
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Notes on Vintage Analysis
• Vintage analysis is just one way of collecting and analyzing historical
loss data
• Other methods may be more appropriate for different portfolios
© 2015 Federal Reserve Bank of St. Louis
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Acronyms
•
•
•
•
ALLL: Allowance for loan and lease losses
CECL: Current expected credit loss
EOY: End of year
FASB: Financial Accounting Standards Board
© 2015 Federal Reserve Bank of St. Louis
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Question and Answer Session
To ask a question:
• Email your question to:
[email protected]
• Use the chat feature in the webinar
(The “Ask Question” button on bottom of your screen)
• Please note: Questions that were submitted in
advance of the session will receive priority.
© 2015 Federal Reserve Bank of St. Louis
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Thank you for joining us.
For any post-session comments or questions,
please contact us at:
[email protected]
© 2015 Federal Reserve Bank of St. Louis
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