CECL Methodology Q&A Anthology

Sageworksanalyst.com
Neekis Hammond, CPA
Principal - Advisory Services
1
February 16, 2017
CECL Methodology Series
Question and Answer

About the Webinar
• We will address as many questions as we can
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2

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Disclaimer
4
This presentation may include statements that constitute “forward-looking statements” relative to
publicly available industry data. Forward-looking statements often contain words such as “believe,”
“expect,” “plans,” “project,” “target,” “anticipate,” “will,” “should,” “see,” “guidance,” “confident” and
similar terms. There can be no assurance that any of the future events discussed will occur as
anticipated, if at all, or that actual results on the industry will be as expected. Sageworks is not
responsible for the accuracy or validity of this publicly available industry data, or the outcome of the use
of this data relative to business or investment decisions made by the recipients of this data. Sageworks
disclaims all representations and warranties, express or implied. Risks and uncertainties include risks
related to the effect of economic conditions and financial market conditions; fluctuation in commodity
prices, interest rates and foreign currency exchange rates. No Sageworks employee is authorized to
make recommendations or give advice as to any course of action that should be made as an outcome
of this data. The forward-looking statements and data speak only as of the date of this presentation
and we undertake no obligation to update or revise this information as of a later date.

Neekis Hammond, CPA
Principal - Advisory Services
About Today’s Presenters
5

Agenda
• Series Overview
• Question and Answer
• Data
• Contractual Life
• Segmentation
• Methodology
• General/Miscellaneous
6

Series Overview
• Thursday, February 16th, 2017, 2-3 p.m.: CECL Methodology Q&A
• Thursday, February 23, 2017, 2-3 p.m.: Unfunded Commitments & Construction Loan CECL
Methodologies
• Thursday, March 9, 2017, 2-3 p.m.: Forecasting with CECL
• Thursday, March 16th, 2017, 2-3 p.m.: CECL Calculations in a Software Environment
• Thursday, March 23, 2017, 2-3 p.m.: Disclosures with CECL
Sign up at: web.sageworks.com/cecl-methodology-webinar-series/
7

Agenda
• Series Overview
• Data
• Segmentation
• Methodology
8

Data
Questions related to the amount and type of historical data:
• How much historical data is required?
• How many historical periods do I need to include; do they need to be stored monthly, quarterly, or
annually?
• We have call report data going back n years; do we have enough data?
• We have loan number, balance, and loan term information stored; is this what you’re referring to?
• We have origination information, loan number, loan balance, and loan-level chargeoff data; what
else are we missing?
9

Data
• How many historical periods do I need to include; do they need to be stored monthly, quarterly,
or annually?
10

Data
11
1.04%
0.96%
1.14%
0.84%
0.70%
0.54%
0.51%
0.43%
0.34%
0.51%
0.43%
0.34%
6/30/2017 9/30/2017 12/31/2017

Data
How much historical data is required?
How many historical periods do we need to include; do they need to be stored monthly, quarterly, or
annually?
• ASU 326-20-55-20: Community Bank A, after considering underwriting standards and the
contractual terms of loans that existed over the historical period, believes that the most recent 10-
year period is reasonable.
• 2009/2010 to 2019/2020
12

Data
13
When Will the Amendments Be Effective?
For public business entities that are U.S. Securities and Exchange Commission(SEC) filers, the
amendments in this Update are effective for fiscal years beginning after December 15, 2019,
including interim periods within those fiscal years. For all other public business entities, the
including interim periods within those fiscal years. For all other entities, including not-for-profit
entities and employee benefit plans within the scope of Topics 960 through 965 on plan accounting,
the amendments in this Update are effective for fiscal years beginning after December 15, 2020, and
interim periods within fiscal years beginning after December 15, 2021. All entities may adopt the
amendments in this Update earlier as of the fiscal years beginning after December 15, 2018,
including interim periods within those fiscal years. An entity will apply the amendments in this
Update through a cumulative-effect adjustment to retained earnings as of the beginning of the first
reporting period in which the guidance is effective (that is, a modified-retrospective approach).

Data
14
When Will the Amendments Be Effective?
For public business entities that are U.S. Securities and Exchange Commission(SEC) filers, the
including interim periods within those fiscal years. For all other public business entities, the
including interim periods within those fiscal years. For all other entities, including not-for-profit
entities and employee benefit plans within the scope of Topics 960 through 965 on plan accounting,
the amendments in this Update are effective for fiscal years beginning after December 15, 2020, and
interim periods within fiscal years beginning after December 15, 2021. All entities may adopt the
amendments in this Update earlier as of the fiscal years beginning after December 15, 2018,
including interim periods within those fiscal years. An entity will apply the amendments in this
Update through a cumulative-effect adjustment to retained earnings as of the beginning of the first
reporting period in which the guidance is effective (that is, a modified-retrospective approach).

Data
16
3 Yr.
4 Yr.

3 Yr.
4 Yr.
Data
17
3 Yr.
4 Yr.

Data
• How many historical periods do we need to include; do they need to be stored monthly, quarterly,
or annually?
18

Data
19
1.04%
0.96%
1.14%
0.84%
0.70%
0.54%
0.51%
0.43%
0.34%
2.37%
2.19%
2.59%
1.90%
1.59%
1.22%
1.16%
0.98%
0.77%
12/31/2015 3/31/2016 6/30/2016 9/30/2016 12/31/2016 3/31/2017 6/30/2017 9/30/2017 12/31/2017
3 Year Loss Rate Sum of Annual Loss Rate

Data
or annually?
• We have loan number, balance, and loan term information stored; is this what you’re referring
to?
20

Data
21
14.1%
0.0%
0.2%
0.1%
0.5%
0.8%
2.4%
12.6%
24.0%
0 1 2 3 4 5 6 7 8
0.69% 0.65%
1.36%
1.24%
1.06%
0.97% 1.04%
0.95% 0.91%
9/30/2011 12/31/2011 3/31/2012 6/30/2012 9/30/2012 12/31/2012 3/31/2013 6/30/2013 9/30/2013
36 MONTH LOSS RATE - MIGRATION

Data
or annually?
22

Data
ASU 326:
• 20-55-10 through 14 “addresses the meaning of the term portfolio segment
• 20-55-15 through 16 “addresses the application of the term credit quality indicator
• “as of the balance sheet date, the entity should use the most current information it has obtained”
23
http://www.fasb.org/jsp/FASB/FASBContent_C/CompletedProjectPage&cid=1176168232014

Agenda
• Series Overview
• Data
• Segmentation
• Methodology
24

Contractual Life
Questions related to determination and utilization of contractual life:
• How do you calculate the average life for the loan? Can you go into more details on how it is
related to attrition analysis?
• Is attrition count based or dollar based?
• When do we use a prepayment rate such as CPR or SMM and when do we use expected life?
• How do you calculate the prepayment rate? Can you go into more detail on how it is calculated?
25

Contractual Life
• How do you calculate the average life for the loan? Can you go into more detail on how it is
related to attrition analysis?
26

Contractual Life
27

Contractual Life
28

Contractual Life
• How do you calculate the average life for the loan? Can you go more details on how it is related to
attrition analysis?
29

Contractual Life
Is attrition count based or dollar based?
• In a sample of N equally likely outcomes mathematicians assign a chance (or weight) of 1/N to
each outcome
• The probability of an event is defined as the number of certain outcomes divided by the total
number of equally likely outcomes in the sample space of the experiment.
• Using a dollar based approach implies an already determined (supportable) correlation to attrition
rates.
• 11 Loans; 10 @ $1, and 1 @ $90. $90 exits the portfolio over the analysis period. Should we then assume 9 of the
remaining 10 loans will exit in the following quarter?
• If dollars are correlated to behavior, that implies a risk variance between loans within a pool that is, by definition,
intended to contain loans with similar risk characteristics
30

Contractual Life
attrition analysis?
31

Contractual Life
32
LOAN NUMBER 4.A CAPITAL EXPENDITURES
GL Balance 115,000,000.00
Segment 4.a CapEx
Subsegment Pass
Date 12/31/2015
Interest Rate 5.50%
Discount Rate 1.53%
Payment Type Balloon
Payment Amt 3,472,528.71
Mat Date 1/31/2020
Nper 36
Renewal Assumption (months) -
CPR (Conditional Prepayment Rate) 33.00%
SMM (Standard Monthly Mortality) 3.28%
PD (Probability of Default) 2.47%
CDR (Constant Default Rate) 0.21%
LGD (Loss Given Default) 25.44%
Recovery Delay (Foreclosure Lag) 12

Contractual Life
33
LOAN NUMBER 4.A CAPITAL EXPENDITURES
GL Balance 115,000,000.00
Segment 4.a CapEx
Subsegment Pass
Date 12/31/2015
Interest Rate 5.50%
Discount Rate 1.53%
Payment Type Balloon
Payment Amt 3,472,528.71
Mat Date 1/31/2020
Nper 36
Renewal Assumption (months) -
CPR (Conditional Prepayment Rate) 33.00%
SMM (Standard Monthly Mortality) 3.28%
PD (Probability of Default) 2.47%
CDR (Constant Default Rate) 0.21%
LGD (Loss Given Default) 25.44%
Recovery Delay (Foreclosure Lag) 12

115,000,000
113,830,961
4,129,933
108,090,635
9,072,700
2,779,432
1,050,572
3,079,361
123,022,127
GL BALANCE PV DEFAULTED
PRINCIPAL
PRINCIPAL INTEREST PREPAYMENT ESTIMATED
LOSS
ESTIMATED
RECOVERY
CASH FLOW
DISCOUNTED CASH FLOW RESULTS
Contractual Life
34

115,000,000
113,830,961
4,129,933
108,090,635
9,072,700
2,779,432
1,050,572
3,079,361
123,022,127
PRINCIPAL
LOSS
ESTIMATED
RECOVERY
CASH FLOW
Contractual Life
35

Contractual Life
36

Contractual Life
37

Contractual Life
attrition analysis?
38

Contractual Life
39
Date Period Beginning Balance Expected Principal Expected Interest End of Month Balance Principal Collection SMM CPR
TOTAL 113,113,050 13,947,211 1.55% 16.91%
- (150,000,000)
12/31/2015 1 150,000,000 1,065,070 393,750 146,274,115 2,660,815 1.06% 12.04%
1/31/2016 2 146,274,115 1,074,851 383,970 142,534,477 2,664,787 1.09% 12.29%
2/29/2016 3 142,534,477 1,084,667 374,153 138,864,780 2,585,030 1.05% 11.93%
3/31/2016 4 138,864,780 1,094,300 364,520 134,151,365 3,619,114 1.82% 19.76%
4/30/2016 5 134,151,365 1,106,673 352,147 130,100,329 2,944,363 1.37% 15.25%
5/31/2016 6 130,100,329 1,117,307 341,513 125,697,376 3,285,646 1.67% 18.26%
6/30/2016 7 125,697,376 1,128,865 329,956 120,131,821 4,436,690 2.63% 27.39%
7/31/2016 8 120,131,821 1,143,474 315,346 116,324,217 2,664,130 1.27% 14.18%
8/31/2016 9 116,324,217 1,153,469 305,351 111,247,654 3,923,093 2.38% 25.11%

Contractual Life
40
TOTAL 113,113,050 13,947,211 1.55% 16.91%
- (150,000,000)
12/31/2015 1 150,000,000 1,065,070 393,750 146,274,115 2,660,815 1.06% 12.04%
1/31/2016 2 146,274,115 1,074,851 383,970 142,534,477 2,664,787 1.09% 12.29%
2/29/2016 3 142,534,477 1,084,667 374,153 138,864,780 2,585,030 1.05% 11.93%
3/31/2016 4 138,864,780 1,094,300 364,520 134,151,365 3,619,114 1.82% 19.76%
4/30/2016 5 134,151,365 1,106,673 352,147 130,100,329 2,944,363 1.37% 15.25%
5/31/2016 6 130,100,329 1,117,307 341,513 125,697,376 3,285,646 1.67% 18.26%
6/30/2016 7 125,697,376 1,128,865 329,956 120,131,821 4,436,690 2.63% 27.39%
7/31/2016 8 120,131,821 1,143,474 315,346 116,324,217 2,664,130 1.27% 14.18%
8/31/2016 9 116,324,217 1,153,469 305,351 111,247,654 3,923,093 2.38% 25.11%

Contractual Life
41
TOTAL 113,113,050 13,947,211 1.55% 16.91%
- (150,000,000)
12/31/2015 1 150,000,000 1,065,070 393,750 146,274,115 2,660,815 1.06% 12.04%
1/31/2016 2 146,274,115 1,074,851 383,970 142,534,477 2,664,787 1.09% 12.29%
2/29/2016 3 142,534,477 1,084,667 374,153 138,864,780 2,585,030 1.05% 11.93%
3/31/2016 4 138,864,780 1,094,300 364,520 134,151,365 3,619,114 1.82% 19.76%
4/30/2016 5 134,151,365 1,106,673 352,147 130,100,329 2,944,363 1.37% 15.25%
5/31/2016 6 130,100,329 1,117,307 341,513 125,697,376 3,285,646 1.67% 18.26%
6/30/2016 7 125,697,376 1,128,865 329,956 120,131,821 4,436,690 2.63% 27.39%
7/31/2016 8 120,131,821 1,143,474 315,346 116,324,217 2,664,130 1.27% 14.18%
8/31/2016 9 116,324,217 1,153,469 305,351 111,247,654 3,923,093 2.38% 25.11%

Agenda
• Series Overview
• Data
• Segmentation
• Methodology
42

Segmentation
Questions related to segmentation and sub-segmentation:
• We presently segregate our pooled loans by loan type (collateral) and then by risk rating. Under
CECL, would you anticipate segregating by product type, then collateral type and then risk rating?
• Can we simply break out our loan pools by call code?
• If FICO is considered a risk identifier then isn’t the most current FICO the important indicator &
thus 5 years of data with only 2 years of FICO works?
• FICO, as referenced in this question, is analogous with risk rating or any other credit quality indicator
43

Segmentation
CECL, would you anticipate segregating by product type, then collateral type and then risk
rating?
44

Segmentation
ASU 326-20-55-5,12,15
• Payment Structure
• Interest Only, Balloon, Amortizing, etc.
• Contract Term
• 30 yr., 15 yr., 3/1 or 5/1 ARM, Revolving, etc.
• Interest Rate
• Fixed/Variable
• Exposure/Loan Type
• Call Code, Product Type, Loan Purpose, etc.
• Example: 1.c.2.a, 4.a, 1st Lien Residential, 2nd Lien Residential, HELOC, New Auto, Used Auto, Unsecured, commercial
equipment, etc.
45

Segmentation
46

Segmentation
47

Segmentation
CECL, would you anticipate segregating by product type, then collateral type and then risk rating?
48

Segmentation
49

Segmentation
50

Agenda
• Series Overview
• Data
• Segmentation
• Methodology
51

Methodology
Questions related to calculating historical loss experience:
• Vintage
• Do you need to have both the origination date and renewal date? Is it ok to replace the first origination date with the
renewal date when it happens?
• Regarding extensions/renewed loans, do you count the extended/renewed life as part of the life of the loan?
• Migration
• What's optimal for loss rate calculation? 36 months? Longer? Shorter?
• When we are talking about migration analysis, are we only concerned with the beginning credit quality indicator and
how much of that pool moved to loss OR are we following the pool through all grade changes through the life of the
loan?
• Why are you using 3-year old data? Why not use 2014-2016? Sure, you'll have some loans that weren't in existence
for the entire 3-year life of the pool, but that will be the case for any pool. It would seem that 2011-2013 loss data for
setting loss rates for 2017 and forward is nearly worthless?
• The outputs are measuring loss rates, not the migration from one risk rating to another. In my institution loss typically
occur once a credit gets to a specific risk rating. Therefore, loss rates in every risk rating category, except that one, will
essentially be 0. Am I understanding that correctly?
52

Methodology
• Vintage
• Do you need to have both the origination date and renewal date? Is it ok to replace the first origination date with
the renewal date when it happens?
• Migration
loan?
53

Methodology
54

Methodology
55

Methodology
• Vintage
• Migration
loan?
56

Methodology
Regarding extensions/renewed loans, do you count the extended/renewed life as part of the life of
the loan?
ASU 326-20-6, 326-20-50-7, 310-20-35-9,10,11,12
In most cases, a renewal would result in a "new loan" for vintage or any life-of-loan analysis. The
guidance contained within the new standard (ASU 326/CECL) points to a "more than minor" test
contained within ASC 310-20: "This condition would be met if the new loan’s effective yield is at least
equal to the effective yield for such loans and modifications of the original debt instrument are more
than minor."
In practice, more than minor has been determined to be >10% change in NPV. Also, short-term
extensions that facilitate administrative needs are ignored and/or do not meet the "more than
minor" test. For example, a 90-day extension would likely not constitute a new loan while a full 3-
year renewal would.
57

Methodology
58

Methodology
• Vintage
• Migration
loan?
59

Methodology
60

Methodology
61

Methodology
62

Methodology
• Vintage
• Migration
• When we are talking about migration analysis, are we only concerned with the beginning credit quality indicator
and how much of that pool moved to loss OR are we following the pool through all grade changes through the life
of the loan?
63

Methodology
64

Methodology
65

Methodology
66

Methodology
• Vintage
• Migration
• When we are talking about migration analysis are we just concerned with where a loan started, say grade 3 and how
much of that pool moved to loss OR are we following the pool through all grade changes through the life of the loan.
for the entire 3-year life of the pool, but that will be the case for any pool. It would seem that 2011-2013 loss data
for setting loss rates for 2017 and forward is nearly worthless.
67

Methodology
68
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%

Methodology
69
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%

Methodology
• Vintage
• Migration
• When we are talking about migration analysis are we just concerned with where a loan started, say grade 3 and how
much of that pool moved to loss OR are we following the pool through all grade changes through the life of the loan.
setting loss rates for 2017 and forward is nearly worthless.
• The outputs are measuring loss rates, not the migration from one risk rating to another. In my institution loss
typically occur once a credit gets to a specific risk rating. Therefore, loss rates in every risk rating category, except
that one, will essentially be 0. Am I understanding that correctly?
70

Segmentation
71

Segmentation
72

Methodology - Continued
• Probability of Default & Loss Given Default – note that migration principles apply
• We subscribe and/or calculate the probability of default at our institution. Can we use our current probability of
default model?
• Discounted Cash Flow
• We’re approaching $10B and are looking for a solution for DFAST and CECL. Which approach is ideal for cross
application?
• Our institution is only $500MM, we don’t need to do complex modeling such as DCF; correct?
73
Note – This is not a complete list of all methodologies that can be used to determine historical loss experience

default model?
application?
74

1.25
3.25
3.75
2.15
1.75
4.25
WORKING CAPITAL
LOC
EQUIPMENT VEHICLE CREDIT CARD CONSTRUCTION SBA
AVERAGE LIFE
Evaluating Risk Characteristics
75

Evaluating Risk Characteristics
76
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
ANNUAL LOSS RATE & EXPECTED LOSS RATE RELATIVE TO AVERAGE LIFE
Annual Loss Rate Expected Loss

default model?
application?
77

Methodology
78

Methodology
79

0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
Q12004
Q22004
Q32004
Q42004
Q12005
Q22005
Q32005
Q42005
Q12006
Q22006
Q32006
Q42006
Q12007
Q22007
Q32007
Q42007
Q12008
Q22008
Q32008
Q42008
Q12009
Q22009
Q32009
Q42009
Q12010
Q22010
Q32010
Q42010
Q12011
Q22011
Q32011
Q42011
Q12012
Q22012
Q32012
Q42012
Q12013
Q22013
Q32013
Q42013
Q12014
Q22014
Q32014
Q42014
Q12015
Q22015
Q32015
Q42015
Q12016
Q22016
Q32016
Q42016
Q12017
Q22017
Q32017
Q42017
Q12018
Q22018
Q32018
Q42018
Q12019
BASELINE FORECAST
12 Month Average (Commercial & Industrial (C&I) Loans - Charge-Offs (% of Average Loans)) Max Standard Error Multi Regression Estimate
Methodology
80

Methodology
81
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
Q12004
Q22004
Q32004
Q42004
Q12005
Q22005
Q32005
Q42005
Q12006
Q22006
Q32006
Q42006
Q12007
Q22007
Q32007
Q42007
Q12008
Q22008
Q32008
Q42008
Q12009
Q22009
Q32009
Q42009
Q12010
Q22010
Q32010
Q42010
Q12011
Q22011
Q32011
Q42011
Q12012
Q22012
Q32012
Q42012
Q12013
Q22013
Q32013
Q42013
Q12014
Q22014
Q32014
Q42014
Q12015
Q22015
Q32015
Q42015
Q12016
Q22016
Q32016
Q42016
Q12017
Q22017
Q32017
Q42017
Q12018
Q22018
Q32018
Q42018
Q12019
ADVERSE FORECAST

Methodology
82
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
Q12004
Q22004
Q32004
Q42004
Q12005
Q22005
Q32005
Q42005
Q12006
Q22006
Q32006
Q42006
Q12007
Q22007
Q32007
Q42007
Q12008
Q22008
Q32008
Q42008
Q12009
Q22009
Q32009
Q42009
Q12010
Q22010
Q32010
Q42010
Q12011
Q22011
Q32011
Q42011
Q12012
Q22012
Q32012
Q42012
Q12013
Q22013
Q32013
Q42013
Q12014
Q22014
Q32014
Q42014
Q12015
Q22015
Q32015
Q42015
Q12016
Q22016
Q32016
Q42016
Q12017
Q22017
Q32017
Q42017
Q12018
Q22018
Q32018
Q42018
Q12019
SEVERLEY ADVERSE FORECAST

Methodology
83
External Factor (National Data) R Square * Slope ** Lead (months)
Real GDP growth 20.51% -0.02% 18
Nominal GDP growth 27.45% -0.02% 18
Real disposable income growth 0.85% 0.00% 18
Nominal -disposable income growth 2.51% 0.00% 18
Unemployment rate 54.64% 0.05% 3
CPI inflation rate 0.12% 0.00% 6
3-month Treasury rate 9.56% -0.02% 6
5-year Treasury yield 0.62% -0.01% 6
10-year Treasury yield 2.45% 0.02% 6
BBB corporate yield 42.89% 0.07% 12
Mortgage rate 0.59% 0.01% 6
Prime rate 10.71% -0.02% 6
Dow Jones Total Stock Market Index (Level) 31.62% 0.00% 18
House Price Index (Level) 39.44% 0.00% 6
Commercial Real Estate Price Index (Level) 44.19% 0.00% 6
Market Volatility Index (Level) 37.21% 0.01% 18
Multi-Regression Statistics Result
Adjusted R Square 79.65%
Standard Error 0.05%
Observations 49
Regression Utilization Coefficients ***
Intercept 0.04%
Commercial Real Estate Price Index (Level) 0.00%
Unemployment rate 0.03%
BBB corporate yield 0.04%
Market Volatility Index (Level) 0.00%

default model?
application?
• We’re a small institution, we don’t need to do complex modeling such as DCF; correct?
84

Methodology
85

Methodology
86

Methodology
87
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%

115,000,000
113,830,961
123,022,127
GL BALANCE PV CASH FLOW
DCF Analysis
88

DCF Analysis
89
115,000,000
113,830,961
1,169,039
123,022,127
GL BALANCE PV RESERVE CASH FLOW

115,000,000
113,830,961
4,129,933
108,090,635
9,072,700
2,779,432
1,050,572
3,079,361
123,022,127
PRINCIPAL
LOSS
ESTIMATED
RECOVERY
CASH FLOW
DCF Analysis
90

Agenda
• Series Overview
• Data
• Segmentation
• Methodology
91

General/Miscellaneous
General questions regarding the new standard and the impact:
• Some claim CECL may drive an increase in reserves, as much as doubling, or more. Should bank
and credit unions worry how their data will be incorporated into stress testing?
• “CECL is being delayed…”
• “New administration will abolish CECL with regulatory reform…”
• “ABA advocating to delay implementation…”
92

93

Credit Unions are particularly vulnerable to the new standard given the industries application of
qualitative and environmental factors and/or the relationship between annual loss rates and reserve
levels:
94
Greater Than
1 Year in Q&E
46%Less Than 1
Year in Q&E
54% Greater Than
6 months in
Q&E
64%
Less Than 6
months in
Q&E
36%

Transition Planning and Execution
95

CECL Methodology Q&A Anthology

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