1. Calculating an Estimate Based on the Static Pool (Vintage) Model
Dan Price
President
Twenty Twenty Analytics
(352)

2. Overview FASB Example Real World Example
Vintage analysis. The vintage analysis is heavily based on the age of the financial asset. Financial assets with similar age are pooled together based on similar risk characteristics, and the use of loss curves or other patterns would typically be utilized in forecasting estimated credit losses
Static pool analysis. The static pool method is easily confused with the vintage analysis. The static pool analysis is based on pools of financial assets with similar risk characteristics and originated within a similar period of time.

3. Static Pool (Vintage) Analysis
Historical Loan Originations
Step 1
Origination Balance
Calendar Year of Origination
Charge Off Data
Step 2
Charge Off
Timing of Charge Off
Project Future Losses
Step 3
Understand Remaining Loan Life
Adjust Historical Results to Reflect Changes
Compile data on all of your originations
Active loans
Loans that have paid off
Loans that have charged off
Calculate historical-losses for each vintage year
Apply loss percentages from older vintage years to newer vintage years

4. FASB Example Year 1 Year 2 Year 3 Year 4 Total 2001 50 120 140 30 340
Year 1
Year 2
Year 3
Year 4
Total
2001 50
120
140 30
340
2002 40
2003
110
150
330
2004 60
360
2005
130
170
400
2006 70
180
2007 80
220
2008
2009
Row Header: Year of Origination
Column Header: Year of a loan’s life
Data Presentation: Sum of charge offs
In narrative terms and assuming the charge offs are presented in thousands, you might say “For loans originated in calendar year 2007, Approximately $80 thousand and $140 thousand in balance charged off in Year 1 and Year 2, respectively”

5. FASB Example Year 1 Year 2 Year 3 Year 4 Total CECL 2001 50 120 140 30
Year 1
Year 2
Year 3
Year 4
Total
CECL
2001 50
120
140 30
340
2002 40
2003
110
150
330
2004 60
360
2005
130
170
400
2006 70
180
460
2007 80
190
480
260
2008
200
500
430
2009
160
510
55-31 In estimating expected credit losses on the remaining outstanding loans at December 31, 20X9, Bank C considers its historical loss information. It notes that the majority of losses historically emerge in Year 2 and Year 3 of the loans. It notes that historical loss experience has worsened since 20X3 and that loss experience for loans originated in 20X6 has already equaled the loss experience for loans originated in 20X5 despite the fact that the 20X6 loans will be outstanding for one additional year as compared with those originated in 20X5.
In considering current conditions and reasonable and supportable forecasts, Bank C notes that :
- there is an oversupply of used farm equipment in the resale market that is expected to continue, thereby putting downward pressure on the resulting collateral value of equipment.
- It also notes that severe weather in recent years has increased the cost of crop insurance and that this trend is expected to continue.

6. Diverse Credit Quality
Multiple Loan Pools
Diverse Credit Quality
Impact of Timing
Sample Size Issues
FASB
Falls Short

7. Real World Example - Autos
2009
2010
2011
2012
2013
2014
2015
2016
2017
Grand Total
Originated Balances (in 000s)
18,864.8
31,055.5
68,659.6
115,641.9
124,326.3
155,252.2
157,787.0
191,970.7
132,688.4
996,246.4
Charge Offs (in 000s)
Year 1
0.0
63.4
306.1
429.4
456.6
386.8
1,642.2
Year 2
89.4
483.4
529.5
763.5
1,368.0
166.4
3,400.2
Year 3
204.5
251.1
297.6
653.6
102.7
1,509.5
Year 4
11.2
80.1
138.0
248.4
254.0
21.5
753.2
Thereafter
49.7
17.9
13.6
81.0
48.6
210.6
60.9
97.9
445.5
1,127.2
1,435.9
1,868.0
1,927.3
553.2
7,515.8
% Charged Off
0.05%
0.25%
0.28%
0.29%
0.20%
0.00%
0.16%
0.13%
0.42%
0.43%
0.49%
0.87%
0.09%
0.34%
0.30%
0.22%
0.24%
0.07%
0.15%
0.06%
0.26%
0.21%
0.01%
0.08%
0.02%
0.04%
0.32%
0.65%
0.97%
1.15%
1.20%
1.22%
0.75%
Starting Point – This is the information that you have available to you. Keep in mind that this is a real world example.
The red highlighted boxes indicate incomplete information. Essentially, charge off data wasn’t being tracked granularly enough until 2012
FDIC/FRB/NCUA/OCC CECL FAQ. Will the agencies require institutions to reconstruct data from earlier periods that are not reasonably available in order to implement CECL? [September 2017] No. The agencies will not require institutions to undertake efforts to obtain or reconstruct data from previous periods that are not reasonably available without undue cost and effort. However, an institution may decide it would be beneficial to do so to more effectively implement CECL.
The yellow highlighted boxes indicate incomplete years. The data is good, but loans in those periods aren’t yet complete. For example, loans originated in 2017 haven’t fully completed their first years

8. Real World Example
What we’re doing here is making estimates for these incomplete years. For our purposes here, we’re going to assume that half of historical averages charge off

9. Real World Example 2012 2013 2014 2015 2016 2017 Grand Total
2012
2013
2014
2015
2016
2017
Grand Total
Originated Balances
115,641.9
124,326.3
155,252.2
157,787.0
191,970.7
132,688.4
996,246.4
% Charged Off
Year 1
0.05%
0.25%
0.28%
0.29%
0.20%
0.13%
0.16%
Year 2
0.42%
0.43%
0.49%
0.87%
0.34%
Year 3
0.22%
0.24%
0.15%
Year 4
0.21%
0.10%
0.08%
Thereafter
0.07%
0.02%
0.97%
1.14%
1.29%
1.30%
0.48%
0.75%
This slide is simply to illustrate that completed process. We’re projecting expected losses over the next half-year for each of these vintage years

10. Real World Example
From here on out we’re projecting losses for the grey highlighted cells. These life cycle years within each vintage year have not yet begun, so we’ll need to estimate a full year’s loss rate. This is an exceptionally judgmental process, one in which you’ll want to consider
- Historical Results
Changes in microeconomic and macroeconomic factors that impact future losses
Assuming there haven’t been any significant changes in macroeconomic factors, you can see that vintage year 2014 has traditionally been riskier than This would suggest that charge offs *Thereafter* may be greater than historical. Our estimate for 2014 thereafter utilizes the same historical average as 2013, but because charge offs in 2014 were approximately 30% higher than 2013, we’re increasing expectations by 30%

11. Real World Example 2012 2013 2014 2015 2016 2017 Grand Total
2012
2013
2014
2015
2016
2017
Grand Total
Originated Balances
115,641.9
124,326.3
155,252.2
157,787.0
191,970.7
132,688.4
996,246.4
% Charged Off
Year 1
0.05%
0.25%
0.28%
0.29%
0.20%
0.13%
0.16%
Year 2
0.42%
0.43%
0.49%
0.87%
0.55%
0.34%
Year 3
0.22%
0.24%
0.15%
Year 4
0.21%
0.10%
0.32%
0.08%
Thereafter
0.07%
0.02%
0.06%
0.09%
0.03%
0.97%
1.14%
1.35%
1.71%
0.93%
1.13%
0.75%
This slide illustrates the completed process, but it’s important to point out some judgment that was made here.
2014 and 2015 has considerably higher loss rates than years prior, so expectations were adjusted upward for these years. Risk appears to have come back down a bit in vintage years 2016 and 2017, so those loss rates were tapered back down.
Because of the minimal loss history on vintage years 2016 and 2017, an auditor would undoubtedly question the decision to reduce expected losses in these pools. It would be important to substantiate this decision by, for example, comparing originating credit scores or other underwriting criteria from 2016 or 2017 loans to that of riskier vintage years.

12. Real World Example 2012 2013 2014 2015 2016 2017 Grand Total
2012
2013
2014
2015
2016
2017
Grand Total
Originated Balances
115,641.9
124,326.3
155,252.2
157,787.0
191,970.7
132,688.4
996,246.4
% Charged Off
Year 1
0.13%
0.16%
Year 2
0.28%
0.55%
0.34%
Year 3
0.15%
0.20%
Year 4
0.10%
0.32%
0.22%
0.08%
Thereafter
0.02%
0.06%
0.09%
0.03%
0.00%
0.73%
1.13%
0.75%
Expected Losses
0.0
27.9
253.6
867.6
1,395.0
1,497.6
4,041.8
Previously Incurred
1,127.2
1,435.9
1,868.0
1,927.3
553.2
6,911.5
Aggregate Expected Losses
1,463.8
2,121.6
2,794.9
1,948.1
10,953.3
Aggregate Expected Loss Rate
0.97%
1.18%
1.37%
1.77%
1.01%
1.10%
Now that you have projected your future loss rates, the next step would be to use those rates to project actual dollar amount of losses.
Expected Losses is your CECL reserve for this portfolio segment. For reference purposes, I have also illustrated previously incurred losses for each vintage year (historical)
Along with the inferred total losses for each vintage and loss rate.
This example assumes stable Q&E. This is one of those things you will have to approach on a case by case basis. Most economic scenarios indicate that it’s the expectation that the economy will continue to improve. My personal standpoint on this is that if you have enough data to support a full economic life cycle (e.g. data that includes recession impact), including those inflated averages in your historical analysis will account for the potential that the economy worsens.

13. What Can Go Wrong? Inconsistent Numerator and Denominator
Treating Incomplete Data as Complete
Inadequate
Loss
Experience
Inconsistent Numerator and Denominator – You’re measuring charge offs as a percentage of balance originated, NOT CURRENT BALANCE. You should be applying those percentage to the original balance, not the current balance
Treating Incomplete Data as Complete – when you’re evaluating your historical loss rates and developing your of future estimates, be sure to focus on vintage years and periods within each vintage year’s life cycle where both origination data and charge off data are materially complete. That means that both (a) you were able to successfully collect the data and (b) that period is completed and you don’t expect more charge offs to occur within that period

14. What Can Go Wrong?
2009
2010
2011
2012
2013
2014
2015
2016
2017
Grand Total
Originated Balances
(in 000s)
16,442.1
13,203.8
18,670.0
38,051.5
41,342.9
35,761.3
59,398.5
50,309.1
30,329.6
303,508.8
Charge Offs (in 000s)
Year 1
0.0
Year 2
Year 3
Year 4
19.6
Thereafter
43.8
134.4
% Charged Off
0.00%
0.05%
0.01%
0.27%
0.82%
0.04%
I wanted to quickly

15. Summary Pros Cons Easy to Visualize and Calculate
Utilizes Historical Loss Methodology
Pros
Requires Lots of Assumptions
Disaggregation Dramatically Increases Burdon
Cons
Pros – To me this methodology is the most similar to the current ALLL process, just over a different period of time
Requires lots of Assumptions – All of the models will require lots of assumptions. The challenge with static pooling is that you’re in a situation where those assumptions will need to be applied manually.
If your CECL model is calculated at the loan record level, you can dynamically adjust for the impact of varying credit or collateral quality and also changes in qualitative and environmental factors. With static pooling, it’s harder to formulaically just for those types of Q&E Factors

16. Twenty Twenty Analytics
Thank You!
Questions?
Dan Price
President
Twenty Twenty Analytics
(352)