cecl methodology series - amazon s3methodology+series+kick+off+… · cecl methodology series...

Post on 04-Aug-2020

2 Views

Category:

Documents

0 Downloads

Preview:

Click to see full reader

TRANSCRIPT

Brandon RussellSageworks ALLL Specialist

CECL Methodology Series

P R E S E N T E D B Y

Neekis Hammond, CPASageworks Risk Management Consultant

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

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

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

About Today’s Presenters

Sageworks ALLL Specialist

5

B R A N D O N R U S S E L L

Sageworks Advisory Services

N E E K I S H A M M O N D , C PA

Agenda

• Series Introduction

• Attrition

• Prepayment

• Data Requirements and Considerations

» Vintage

» Migration

» PD/LGD

» DCF

• Questions

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/

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.)

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%

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

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

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

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

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

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

Poll Question.

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

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%

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

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%

#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.

#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

#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

#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

#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

Strongly Recommended Data Elements

Vintage Analysis.

Strongly Recommended Data Elements

Vintage Analysis.

Strongly Recommended Data Elements (continued)

Vintage Analysis.

Vintage Analysis.

Strongly Recommended Data Elements (continued)

Recommended Data Elements

Vintage Analysis.

Recommended Data Elements

Vintage Analysis.

Poll Question.

#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

#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

#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

#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

Strongly Recommended Data Elements

Migration Analysis.

Strongly Recommended Data Elements

Migration Analysis.

Strongly Recommended Data Elements (continued)

Migration Analysis.

Migration Analysis.

Strongly Recommended Data Elements (continued)

Recommended Data Elements

Migration Analysis.

Recommended Data Elements

Migration Analysis.

Poll Question.

#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.

#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

#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

#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

#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

Strongly Recommended Data Elements

PD.

Strongly Recommended Data Elements

PD.

Strongly Recommended Data Elements (continued)

PD.

PD.

Strongly Recommended Data Elements (continued)

Recommended Data Elements

PD.

Recommended Data Elements

PD.

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.

Poll Question.

#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

#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

#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

#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

#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

#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

#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

#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

Strongly Recommended Data Elements

DCF.

Strongly Recommended Data Elements

DCF.

Strongly Recommended Data Elements (Continued)

DCF.

Strongly Recommended Data Elements (Continued)

DCF.

Strongly Recommended Data Elements (Continued)

DCF.

Strongly Recommended Data Elements (Continued)

DCF.

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

Poll Question.

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 RussellSageworks ALLL Specialist

Brandon.Russell@Sageworks.com

Neekis Hammond, CPASageworks Advisory Services

Neekis.Hammond@Sageworks.com

PRESENTERS

top related