The document outlines a webinar presented by Sageworks on CECL methodology, addressing critical components such as attrition, prepayment, data requirements, and forecasting. It emphasizes the importance of methodologies in risk management for financial institutions and details the agenda for upcoming sessions. Legal disclaimers regarding the use of forward-looking statements and the accuracy of industry data are also included.

1. Brandon Russell
Sageworks ALLL Specialist
CECL Methodology Series
P R E S E N T E D B Y
Neekis Hammond, CPA
Sageworks Risk Management Consultant

2. About the Webinar
2
• Ask questions throughout the session using the
GoToWebinar control panel
• We will answer as many questions as we can at the
end of the presentation

3. About Sageworks
• Risk management thought leader
for institutions and examiners
• Regularly featured in national and
trade media
• Loan portfolio and risk
management solutions
• More than 1,000 financial
institution clients
• Founded in 1998
3

4. Disclaimer
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.
4

5. About Today’s Presenters
Sageworks ALLL Specialist
5
B R A N D ON R U SSELL
Sageworks Advisory Services
N EEK IS H A MMON D , C PA

6. Agenda
• Series Introduction
• Attrition
• Prepayment
• Data Requirements and Considerations
» Vintage
» Migration
» PD/LGD
» DCF
• Questions

7. CECL Methodology Series
• Thursday, January 12, 2017, 2-3 p.m.: CRE Pool CECL Methodologies
• Thursday, January 26: Consumer Pool CECL Methodologies
• Thursday, February 9, 2017, 2-3 p.m.: C&I Pool CECL Methodologies
• 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 23, 2017, 2-3 p.m.: Disclosures with CECL
Sign up at: web.sageworks.com/cecl-methodology-webinar-series/

8. What is the purpose?
Attrition Analysis.
• Utilization
» Most methodologies require a life assumption prior to pool-level execution
• Support
» Very material to the historical loss experience, and will be scrutinized
• Compliance
» In order to accommodate key components of the standard, it is important that the logic aligns with
certain provisions
• Renewals
• Material modifications
• Maturity
• Balance considerations (payoff, chargeoff, etc.)

9. Determining the “life” of each pool
Attrition Analysis.
Product Type Attrition Active Attrition Annual Rate
Commercial RE 417 7,514 6% 20%
Commercial/Ag 1,095 6,673 16% 51%
Consumer - Auto 1,222 10,572 12% 39%
Farm RE 76 1,483 5% 19%
HELOC 522 10,753 5% 18%
RE Construction 61 710 9% 30%
RE Mortgage 578 13,599 4% 16%
Grand Total 3,971 51,311 8% 28%

10. Determining the “life” of each pool
Attrition Analysis.
Date Loan # Call Code Balance Mat Date Ren Date Exit
12/31/2010 1 1.c.2.a 100 12/31/2015
12/31/2010 2 1.e.1 100 6/30/2011
12/31/2010 3 4.a 100 12/31/2015
3/31/2011 1 1.c.2.a 90 12/31/2015
3/31/2011 2 1.e.1 90 6/30/2011
3/31/2011 3 4.a 90 12/31/2015
6/30/2011 1 1.c.2.a 0 12/31/2015 Y
6/30/2011 2 1.e.1 80 6/30/2011 Y
6/30/2011 3 4.a 80 12/31/2015
9/30/2011 1 1.c.2.a 0 12/31/2015
9/30/2011 2 1.e.1 70 6/30/2011
9/30/2011 3 4.a 70 12/31/2015 9/30/2011 Y

11. Determining the “life” of each pool
Attrition Analysis.
Date Loan # Call Code Balance Mat Date Ren Date Exit
12/31/2010 1 1.c.2.a 100 12/31/2015
12/31/2010 2 1.e.1 100 6/30/2011
12/31/2010 3 4.a 100 12/31/2015
3/31/2011 1 1.c.2.a 90 12/31/2015
3/31/2011 2 1.e.1 90 6/30/2011
3/31/2011 3 4.a 90 12/31/2015
6/30/2011 1 1.c.2.a 0 12/31/2015 Y
6/30/2011 2 1.e.1 80 6/30/2011 Y
6/30/2011 3 4.a 80 12/31/2015
9/30/2011 1 1.c.2.a 0 12/31/2015
9/30/2011 2 1.e.1 70 6/30/2011
9/30/2011 3 4.a 70 12/31/2015 9/30/2011 Y

12. Determining the “life” of each pool
Attrition Analysis.
Date Loan # Call Code Balance Mat Date Ren Date Exit
12/31/2010 1 1.c.2.a 100 12/31/2015
12/31/2010 2 1.e.1 100 6/30/2011
12/31/2010 3 4.a 100 12/31/2015
3/31/2011 1 1.c.2.a 90 12/31/2015
3/31/2011 2 1.e.1 90 6/30/2011
3/31/2011 3 4.a 90 12/31/2015
6/30/2011 1 1.c.2.a 0 12/31/2015 Y
6/30/2011 2 1.e.1 80 6/30/2011 Y
6/30/2011 3 4.a 80 12/31/2015
9/30/2011 1 1.c.2.a 0 12/31/2015
9/30/2011 2 1.e.1 70 6/30/2011
9/30/2011 3 4.a 70 12/31/2015 9/30/2011 Y

13. Determining the “life” of each pool
Attrition Analysis.
Date Loan # Call Code Balance Mat Date Ren Date Exit
12/31/2010 1 1.c.2.a 100 12/31/2015
12/31/2010 2 1.e.1 100 6/30/2011
12/31/2010 3 4.a 100 12/31/2015
3/31/2011 1 1.c.2.a 90 12/31/2015
3/31/2011 2 1.e.1 90 6/30/2011
3/31/2011 3 4.a 90 12/31/2015
6/30/2011 1 1.c.2.a 0 12/31/2015 Y
6/30/2011 2 1.e.1 80 6/30/2011 Y
6/30/2011 3 4.a 80 12/31/2015
9/30/2011 1 1.c.2.a 0 12/31/2015
9/30/2011 2 1.e.1 70 6/30/2011
9/30/2011 3 4.a 70 12/31/2015 9/30/2011 Y

14. Determining the “life” of each pool
Attrition Analysis.
Date Loan # Call Code Balance Mat Date Ren Date Exit
12/31/2010 1 1.c.2.a 100 12/31/2015
12/31/2010 2 1.e.1 100 6/30/2011
12/31/2010 3 4.a 100 12/31/2015
3/31/2011 1 1.c.2.a 90 12/31/2015
3/31/2011 2 1.e.1 90 6/30/2011
3/31/2011 3 4.a 90 12/31/2015
6/30/2011 1 1.c.2.a 0 12/31/2015 Y
6/30/2011 2 1.e.1 80 6/30/2011 Y
6/30/2011 3 4.a 80 12/31/2015
9/30/2011 1 1.c.2.a 0 12/31/2015
9/30/2011 2 1.e.1 70 6/30/2011
9/30/2011 3 4.a 70 12/31/2015 9/30/2011 Y

15. Determining the “life” of each pool
Attrition Analysis.
Date Loan # Call Code Balance Mat Date Ren Date Exit
12/31/2010 1 1.c.2.a 100 12/31/2015
12/31/2010 2 1.e.1 100 6/30/2011
12/31/2010 3 4.a 100 12/31/2015
3/31/2011 1 1.c.2.a 90 12/31/2015
3/31/2011 2 1.e.1 90 6/30/2011
3/31/2011 3 4.a 90 12/31/2015
6/30/2011 1 1.c.2.a 0 12/31/2015 Y
6/30/2011 2 1.e.1 80 6/30/2011 Y
6/30/2011 3 4.a 80 12/31/2015
9/30/2011 1 1.c.2.a 0 12/31/2015
9/30/2011 2 1.e.1 70 6/30/2011
9/30/2011 3 4.a 70 12/31/2015 9/30/2011 Y

16. Poll Question.

17. What is the purpose?
Prepayment (SMM & CPR).
• Utilization
» Discounted Cash Flow models represent the best use case for this specific output
• Support
» Very material to the periodic cash flow stream/present value determination
• Compliance
» ASU 326-20-30-6: “An entity shall consider prepayments as a separate input in the method or
prepayments may be embedded in the credit loss information”
• DCF = Separate input
• Migration, PD/LGD, Cumulative and any other “static” method = Hybrid input
• Vintage = Embedded

18. Example calculation – pool summary
Prepayment (SMM & CPR).
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%
9/30/2016 10 111,247,654 1,166,795 292,025 106,689,110 3,391,748 2.00% 21.53%
10/31/2016 11 106,689,110 1,178,761 280,059 103,132,834 2,377,515 1.12% 12.68%
11/30/2016 12 103,132,834 1,188,097 270,724 99,610,720 2,334,017 1.11% 12.55%

19. Prepayment (SMM & CPR).
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%
9/30/2016 10 111,247,654 1,166,795 292,025 106,689,110 3,391,748 2.00% 21.53%
10/31/2016 11 106,689,110 1,178,761 280,059 103,132,834 2,377,515 1.12% 12.68%
11/30/2016 12 103,132,834 1,188,097 270,724 99,610,720 2,334,017 1.11% 12.55%
Example calculation – pool summary

20. Example calculation – pool summary
Prepayment (SMM & CPR).
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%
9/30/2016 10 111,247,654 1,166,795 292,025 106,689,110 3,391,748 2.00% 21.53%
10/31/2016 11 106,689,110 1,178,761 280,059 103,132,834 2,377,515 1.12% 12.68%
11/30/2016 12 103,132,834 1,188,097 270,724 99,610,720 2,334,017 1.11% 12.55%

21. #SageworksSummit
Vintage Analysis.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
21
Vintage Analysis is a method of evaluating the lifetime
credit quality of a loan portfolio by analyzing net charge-
offs in a homogeneous loan pool where the loans share
the same origination period. The method is best used in
the analysis of pools of term debt such as auto and
mortgage portfolios.

22. #SageworksSummit
Vintage Analysis.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
22
Vintage Analysis is a method of evaluating the lifetime
credit quality of a loan portfolio by analyzing net-charge-
offs in a homogeneous loan pool where the loans share
the same origination period. The method is best used in
the analysis of pools of term debt such as auto and
mortgage portfolios.
Lifetime

23. #SageworksSummit
Vintage Analysis.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
23
Vintage Analysis is a method of evaluating the lifetime
credit quality of a loan portfolio by analyzing net-charge-
offs in a homogeneous loan pool where the loans share
the same origination period. The method is best used in
the analysis of pools of term debt such as auto and
mortgage portfolios.
Lifetime
Homogeneous

24. #SageworksSummit
Vintage Analysis.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
24
Vintage Analysis is a method of evaluating the lifetime
credit quality of a loan portfolio by analyzing net charge-
offs in a homogeneous loan pool where the loans share
the same origination period. The method is best used in
the analysis of pools of term debt such as auto and
mortgage portfolios.
Lifetime
Homogeneous
Origination

25. #SageworksSummit
Vintage Analysis.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
25
Vintage Analysis is a method of evaluating the lifetime
credit quality of a loan portfolio by analyzing net charge-
offs in a homogeneous loan pool where the loans share
the same origination period. The method is best used in
the analysis of pools of term debt such as auto and
mortgage portfolios.
Lifetime
Homogeneous
Origination
Term Debt

26. Strongly Recommended Data Elements
Vintage Analysis.

27. Strongly Recommended Data Elements
Vintage Analysis.

28. Strongly Recommended Data Elements (continued)
Vintage Analysis.

29. Vintage Analysis.
Strongly Recommended Data Elements (continued)

30. Recommended Data Elements
Vintage Analysis.

31. Recommended Data Elements
Vintage Analysis.

32. Poll Question.

33. #SageworksSummit
Migration Analysis uses loan-level attributes to track the
movements of loans through the various loan
classifications in order to estimate the percentage of
losses likely to be incurred in a financial institution’s
current portfolio.
Migration & Static Cumulative Loss.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
33

34. #SageworksSummit
Migration Analysis uses loan-level attributes to track the
movement of loans through the various loan
classifications in order to estimate the percentage of
losses likely to be incurred in a financial institution’s
current portfolio.
Migration & Static Cumulative Loss.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
34
loan-level

35. #SageworksSummit
Migration Analysis uses loan-level attributes to track the
movement of loans through the various loan
classifications in order to estimate the percentage of
losses likely to be incurred in a financial institution’s
current portfolio.
Migration & Static Cumulative Loss.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
35
loan-level
movement

36. #SageworksSummit
Migration Analysis uses loan-level attributes to track the
movement of loans through the various loan
classifications in order to estimate the percentage of
losses likely to be incurred in a financial institution’s
current portfolio.
Migration & Static Cumulative Loss.
• Vintage
• Migration & Static
• PD/LGD
• DCF
• Q&A
AGENDA
36
loan-level
movement
classifications

37. Strongly Recommended Data Elements
Migration Analysis.

38. Strongly Recommended Data Elements
Migration Analysis.

39. Strongly Recommended Data Elements (continued)
Migration Analysis.

40. Migration Analysis.
Strongly Recommended Data Elements (continued)

41. Recommended Data Elements
Migration Analysis.

42. Recommended Data Elements
Migration Analysis.

43. Poll Question.

44. #SageworksSummit
PD/LGD.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
44
PD - (probability of default) : the average percentage of
borrowers that default over a defined period of time
LGD - (loss given default): the aggregate subsequent
loss incurred on borrowers that have met the default
criteria as output from the PD analysis.
Displayed/calculated as a percentage of aggregate loss
relative to the exposure at the time of default
PD x LGD calculates the expected loss rate; PD x LGD x
Recorded Investment generates the total dollar amount
of expected losses.

45. #SageworksSummit
PD - (probability of default) : the average percentage of
borrowers that default over a defined period of time
LGD - (loss given default): the aggregate subsequent
loss incurred on borrowers that have met the default
criteria as output from the PD analysis.
Displayed/calculated as a percentage of aggregate loss
relative to the exposure at the time of default
PD x LGD calculates the expected loss rate; PD x LGD x
Recorded Investment generates the total dollar amount
of expected losses.
PD/LGD.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
45
averagePD

46. #SageworksSummit
PD - (probability of default) : the average percentage of
borrowers that default over a certain period of time
LGD - (loss given default): The percentage of exposure
to a bank if the borrower defaults
EAD - (exposure at default): an estimate of the
outstanding amount, or exposure to the bank, in the
event a borrower defaults.
PD x LGD calculates the expected loss rate; PD x LGD x
EAD generates the total dollar amount of expected
losses.
PD/LGD.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
46
averagePD
default

47. #SageworksSummit
PD - (probability of default) : the average percentage of
borrowers that default over a defined period of time
LGD - (loss given default): the aggregate subsequent
loss incurred on borrowers that have met the default
criteria as output from the PD analysis.
Displayed/calculated as a percentage of aggregate loss
relative to the exposure at the time of default
PD x LGD calculates the expected loss rate; PD x LGD x
Recorded Investment generates the total dollar amount
of expected losses.
PD/LGD.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
47
aggregate loss
LGD

48. #SageworksSummit
PD - (probability of default) : the average percentage of
borrowers that default over a defined period of time
LGD - (loss given default): the aggregate subsequent
loss incurred on borrowers that have met the default
criteria as output from the PD analysis.
Displayed/calculated as a percentage of aggregate loss
relative to the exposure at the time of default
PD x LGD calculates the expected loss rate; PD x LGD x
Recorded Investment generates the total dollar amount
of expected losses.
PD/LGD.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
48
aggregate loss
LGD
exposure AT default

49. Strongly Recommended Data Elements
PD.

50. Strongly Recommended Data Elements
PD.

51. Strongly Recommended Data Elements (continued)
PD.

52. PD.
Strongly Recommended Data Elements (continued)

53. Recommended Data Elements
PD.

54. Recommended Data Elements
PD.

55. Not a stand-alone metric - Critical Data Elements
LGD.
Errors in determining default population and/or proper exposure at default
are very common.
Be sure to fully understand the relationship between the default population
being evaluated for LGD. Without proper oversight, LGD can decline rapidly
in periods of accelerated defaults as new defaults have not had time to
experience a charge-off event.
Also, maintaining symmetrical application relative to the analysis can result
in misleading and erroneous outputs.

56. Poll Question.

57. #SageworksSummit
If an entity estimates expected credit losses using
methods that project future principal and interest cash
flows (that is, a discounted cash flow method), the entity
shall discount expected cash flows at the financial
asset’s effective interest rate. When a discounted cash
flow method is applied, the allowance for credit losses
shall reflect the difference between the amortized cost
basis and the present value of the expected cash flows.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
57

58. #SageworksSummit
If an entity estimates expected credit losses using
methods that project future principal and interest cash
flows (that is, a discounted cash flow method), the entity
shall discount expected cash flows at the financial
asset’s effective interest rate. When a discounted cash
flow method is applied, the allowance for credit losses
shall reflect the difference between the amortized cost
basis and the present value of the expected cash flows.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
58
discount

59. #SageworksSummit
If an entity estimates expected credit losses using
methods that project future principal and interest cash
flows (that is, a discounted cash flow method), the entity
shall discount expected cash flows at the financial
asset’s effective interest rate. When a discounted cash
flow method is applied, the allowance for credit losses
shall reflect the difference between the amortized cost
basis and the present value of the expected cash flows.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
59
effective interest rate
discount

60. #SageworksSummit
If an entity estimates expected credit losses using
methods that project future principal and interest cash
flows (that is, a discounted cash flow method), the entity
shall discount expected cash flows at the financial
asset’s effective interest rate. When a discounted cash
flow method is applied, the allowance for credit losses
shall reflect the difference between the amortized cost
basis and the present value of the expected cash flows.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
60
amortized cost basis
effective interest rate
discount

61. #SageworksSummit
If an entity estimates expected credit losses using
methods that project future principal and interest cash
flows (that is, a discounted cash flow method), the entity
shall discount expected cash flows at the financial
asset’s effective interest rate. When a discounted cash
flow method is applied, the allowance for credit losses
shall reflect the difference between the amortized cost
basis and the present value of the expected cash flows.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
61
amortized cost basis
present value
effective interest rate
discount

62. #SageworksSummit
When a discounted cash flow approach is used to estimate
expected credit losses, the change in present value from one
reporting period to the next may result not only from the passage of
time but also from changes in estimates of the timing or amount of
expected future cash flows. An entity that measures credit losses
based on a discounted cash flow approach is permitted to report the
entire change in present value as credit loss expense (or reversal of
credit loss expense). Alternatively, an entity may report the change
in present value attributable to the passage of time as interest
income.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
62

63. #SageworksSummit
When a discounted cash flow approach is used to estimate
expected credit losses, the change in present value from one
reporting period to the next may result not only from the passage of
time but also from changes in estimates of the timing or amount of
expected future cash flows. An entity that measures credit losses
based on a discounted cash flow approach is permitted to report the
entire change in present value as credit loss expense (or reversal of
credit loss expense). Alternatively, an entity may report the change
in present value attributable to the passage of time as interest
income.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
63
provision expense

64. #SageworksSummit
When a discounted cash flow approach is used to estimate
expected credit losses, the change in present value from one
reporting period to the next may result not only from the passage of
time but also from changes in estimates of the timing or amount of
expected future cash flows. An entity that measures credit losses
based on a discounted cash flow approach is permitted to report the
entire change in present value as credit loss expense (or reversal of
credit loss expense). Alternatively, an entity may report the change
in present value attributable to the passage of time as interest
income.
DCF.
• Vintage
• Migration
• PD/LGD
• DCF
• Q&A
AGENDA
64
provision expense
interest income

65. Strongly Recommended Data Elements
DCF.

66. Strongly Recommended Data Elements
DCF.

67. Strongly Recommended Data Elements (Continued)
DCF.

68. Strongly Recommended Data Elements (Continued)
DCF.

69. Strongly Recommended Data Elements (Continued)
DCF.

70. Strongly Recommended Data Elements (Continued)
DCF.

71. Additional Information
DCF.
• Cross Application
» Day 2 Accounting: Current PCI re-estimation requirements available with few changes to the underlying
inputs
» Stress Testing: Period specific assumptions and period specific estimates fit nicely into stress testing
models
» Fair Value: Fair value exploration or classification and measurement requirements are available with
few changes to the underlying inputs
» Loan Pricing: NPV given the return of an alternative investment, fees, expenses, overhead is a valuable
output for loan-decisioning as well as overall portfolio analysis
• Annualized/Peer Data Utilization
» Readily available annual/quarterly peer data or internal data that lacks loan-level detail can be used in
DCF models

72. Poll Question.

73. Q&A
• Follow up email
• ALLL.com
• SageworksAnalyst.com – latest
whitepapers and archived webinars
• SageworksAnalyst.com – product and
advisory services information
• Risk Management Summit 2017 –
September 24-27 in Denver, CO
73
RESOURCES
Brandon Russell
Sageworks ALLL Specialist
Brandon.Russell@Sageworks.com
Neekis Hammond, CPA
Sageworks Advisory Services
Neekis.Hammond@Sageworks.com
PRESENTERS