SAVE THE DATE! 22nd Annual CFO Council Conference The Disneyland Hotel Anaheim, CA May 15 18, 2016

Transcription

1 SAVE THE DATE! 22nd Annual CFO Council Conference The Disneyland Hotel Anaheim, CA May 15 18, 2016

2 2 A Practical Guide to the Allowance for Expected Credit Loss FASB Subtopic

3 Agenda Introduction Calculating the Allowance for Expected Credit Loss Required Disclosures Conclusions Notice: FASB is still deliberating and has not made a final accounting standards update! 3

4 Introduction Allowance for Expected Credit Loss: Main Objective The main objective of the Accounting Standards Update is to provide financial statement users with more decision useful information about an institution s expected credit losses by requiring consideration of a broader range of reasonable and supportable information. 4 More Discussion and Disclosures -No Probable Threshold -Past, Current, and Future

5 Introduction Allowance for Expected Credit Loss: Balance Sheet Statement of Financial Condition (Balance Sheet) Assets Total Loans $100,000,000 Less Allowance for Expected Credit Loss <$2,000,000> Net Loans $98,000,000 Current estimate of cash flows NOT expected to be collected. 5

6 Introduction Allowance for Expected Credit Loss From prior period Dec. 31, 2015Mar. 31, 2016 Beginning Balance $2,100 $2,000 -Charge-offs $700 +Recoveries $100 Balance Before Expected Credit Loss Provision $1,500 +Provision for Expected Credit Loss $500 Ending Balance $2,000 You first calculate the required Allowance for Expected Credit Loss Then use the Provision to balance 6

7 Introduction Allowance for Expected Credit loss: Income Statement Statement of Financial Performance (Income Statement) Interest Income Loans $10,000,000 Etc. $ 1,000,000 Total $11,000,000 Interest Expense Deposits/Shares $ 2,000,000 Etc. $ 0 Total $ 2,000,000 Net Interest Income $ 9,000,000 Less Provision for Expected Credit Loss <$ 500,000> The provision reduces the Net Interest Income Net Interest Income After Provision for Expected Credit Loss $ 8,500,000 7

8 Agenda Introduction Calculating the Allowance for Expected Credit Loss Required Disclosures Conclusion Notice: FASB is still deliberating and has not made a final accounting standards update! 8

9 Steps Step 1. Properly Segment the portfolio Step 2. Decide on Credit Quality Indicator (CQI) to use for each Segment Step 3. Estimate the Expected Loss Rate for each Segment Step 4. Multiply the Expected Loss Rate by the Segment/CQI Balance 9

10 Steps Step 1. Properly segment the portfolio Balance Residential Mortgages Expected Loss Rate Expected Credit Loss A (1) $200,000, % $560,000 B (2) 150,000, % 450,000 C (3) 75,000, % 562,500 D (4) 25,000, % 675,000 E (5) 5,000, % 337,500 Sub-total $455,000, % $2,585,000 Consumer-Auto A (1) B (2) Step 2. Decide on an appropriate Credit Etc. Quality Indicator for each Segment 10 Step 3. Estimate an appropriate Expected Loss Rate, including economic adjustments, for each Segment Step 4. Multiply each Segment/CQI balance by its Expected Loss Rate and then sum each Segment s sub-total to calculate the Allowance for Expected Credit Loss

11 Step 1: Properly Segment the Portfolio The level at which an entity develops and documents a systematic methodology to determine its allowance for credit losses. a. Type of debt instrument b. Industry sector of the borrower c. Risk rate(s). Too much detail Too little detail Find the right balance Example: Business/Commercial SBA Commercial Real Estate Commercial Other Consumer Credit Card Auto Other Secured Other Unsecured Residential First Mortgage Other Other 11

12 Step 2. Decide on which Credit Quality Indicators (CQI) to use for each Segment Credit Quality Indicator Credit Score Loan-to-Value (LTV) Probability of Default (PD) Internal Risk Ratings* Description Credit scores are provided by credit bureau and are updated quarterly. The LTV is based on a loan s combined balance (including senior liens) divided by a current value. The PD calculates a borrower s likelihood of defaulting, expressed between 0% and 100%. The PD model used is a hazard survival model, which is a conditional model that allows probabilities to change based on how long the loan has survived and based on changing loan characteristics. We assign internal risk rating based on *Must show how internal grade/rating relates to likelihood of loss 12

13 Step 2. Decide on which Credit Quality Indicators (CQI) to use for each Segment Segment Business/Commercial: All Consumer: Credit Card Consumer: Auto Consumer: Other Secured Consumer: Other Unsecured Residential: First Mortgage Residential: Other Credit Quality Indicator (CQI) Internal Risk Rating PD PD PD Credit Score PD PD 13

14 Step 3. Estimate the Expected Loss Rate for each Segment: There are three main methods to calculating the Expected Loss Rate 1. Probability of Default Method (PD) 2. Loss Rate Method 3. Discounted Cash Flow Method (DCF) 14

15 Step 3. Estimate the Expected Loss Rate for each Segment Segment Business/Commercial: All Consumer: Credit Card Consumer: Auto Consumer: Other Secured Expected Loss Rate Method Loss Rate-Static Pool PD PD PD Each segment can use a different Expected Loss Rate Consumer: Other Unsecured Residential: First Mortgage Residential: Other Credit Scores PD PD 15

16 Step 3. Estimate the Expected Loss Rate for each Segment Base Loss Rate Economic Adjustments: Current Conditions Economic Adjustments: Reasonable & Supportable Forecasts Expected Loss Rate Think of each method for calculating the Expected Loss Rate in three parts: 1) a base rate, 2) an economic adjustment for current conditions, and 3) an economic adjustment for reasonable and supportable forecasts, even though they may all get rolled up into one or the economic adjustments may apply equally to all segments. 16

17 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method 1. Probability of Default Method (PD) 2. Loss Rate Method 3. Discounted Cash Flow Method (DCF) 17

18 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method There are different A survival default model is type of conditional PD model: types of PD models -Probabilities can change based on how long a loan has survived -Probabilities can change based on changing characteristics of the loan Each major loan type uses a different model Residential Real Estate Model* Auto Loan Model* Credit Card Model* Commercial Real Estate Model* Student Lending Model* Etc* Credit Score + LTV + DTI + BAL. + Unemployment + Home Prices Borrower/Loan Attributes Macro-Economic Factors *Each model has different covariates 18

19 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method For given directional change in the covariate what is the impact on PD? Covariate Direction PD Credit Score Down Up Loan to Value (LTV) Up Up Debt to Income (DTI) Up Up Balance Up Up Home Price Index Down Up Unemployment Rate Up Up 19

20 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method Seven different loans, varying just one component of the model at a time Loan ID Balance ($) Credit Score LTV (%) DTI (%) Unemployment (%) 5 Year Change HPI (%) This is an average loan in the Los Angeles MSA 5 year Estimated PD (%) F100Q % F100Q % F101Q % F104Q % F103Q % F101Q % F199Q % LTV = Loan-to-Value DTI = Debt-to-income HPI = Home Price Index PD = Probability of Default The resulting PD For example, if the credit score declines from 729 to 674, the PD increases from 1.52% to 2.31%. 20

21 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method Including Collateral Value x (1 Selling Costs) (Net Proceeds EAD) / EAD LGD x EAD Senior Liens Collateral Selling Net Expected Loan ID Grade EAD ($) Value ($) Costs (%) Proceeds ($) LGD (%) PD (%) Loss Rate (%) A 100, ,000 25% 82, % 1.5% -0.3% A 200, ,000 25% 180, % 1.3% -0.1% Avg.for Grade A 300, ,000 25% 225, % 0.8% -0.2% A B 200, ,000 25% 210, % 3.0% 0.0% = -0.2% C 100, ,000 25% 90, % 5.0% -0.5% B 200, ,000 25% 112, % 3.3% -1.4% C 95, ,000 25% 85, % 4.5% -0.5% Total/Average 1,195,000 From model (includes Economic Adjustments) PD = Probability of Default LGD (%) = Loss Given Default (on a percentage basis) EAD = Exposure at Default (aka outstanding balances) KEY POINT: Each loan has an Expected Loss Rate, which is then used to derive the Segment/CQI Expected Loss Rate. 21

22 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method Let s look at a real loan over it s lifetime KEY POINT: EAD, Collateral Value, Selling Costs, PD, all can change over the life of the loan! This is important for incorporating forecasts. Loan ID: ($100k original balance) Month EAD ($) Collateral Value ($) Selling Costs (%) Net Proceeds ($) LGD (%) PD (%) 1 99, , % 75, % 0.10% -0.02% 2 99, , % 75, % 0.10% -0.02% 3 98, , % 75, % 0.10% -0.02% 4 98, , % 75, % 0.10% -0.02% 5 98, , % 75, % 0.11% -0.02% 6 97, , % 76, % 0.11% -0.02% , % 108, % 0.09% 0.00% TOTAL 1.50% -0.30% PD = Probability of Default LGD (%) = Loss Given Default EAD = Exposure at Default Expected Loss Rate (%) This loan s contribution to Expected Loss Rate. Effectively $300 ($100,000 x 0.3%) is added to the Allowance on Day 1. 22

23 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method Each loan is then categorized into an appropriate Segment/CQI Balance ($) Residential Mortgages Expected Loss Rate (%) Expected Credit Loss ($) A (1) 600, % 1,200 B (2) 400, % 2,800 C (3) 195, % 975 D (4) 0-0 E (5) 0-0 Sub-total $1,195,000.42% $4,975 Consumer-Auto A (1) B (2) Etc. 23 The Segment/CQI Expected Loss Rate is found by averaging all the applicable individual loans Expected Loss Rates The Expected Credit loss for the Segment/CQI is found by multiplying the Balance by the Expected Loss Rate The sub-total is combined with other Segment/CQI sub-totals and rolled up into the required Allowance for Expected Credit Loss

24 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate Method 1. Probability of Default Method (PD) 2. Loss Rate Method 3. Discounted Cash Flow Method (DCF) 24

25 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate Method 2. Loss Rate Method by Segment by Original Grade by Current Grade What about credit quality distribution? What about credit deterioration? Do all loans start out in high quality tiers? What about credit deterioration? Do all loans end in a low quality tiers? What about the risk inherent in the higher tiers? Probably insufficient Is Static Pool or Loss Migration the solution? 25

26 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate-Static Pool Method Charged-off loans divided by original balance Loss Experience in Year Following Origination Base Loss Rate Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Segment 1: Original Grade A (1) % % 0.0% % 0.0% 0.0% % 0.0% 0.1% 0.1% % 0.0% 0.1% 0.1% 0.2% % 0.0% 0.2% 0.4% 0.0% 0.6% % 0.1% 0.0% 0.2% 0.0% 0.0% 0.3% % 0.2% 0.5% 0.2% 0.1% 0.0% 0.0% 1.1% % 0.3% 0.7% 0.5% 0.0% 0.0% 0.0% 0.0% 1.6% % 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.3% Avg. 0.0% 0.0% 0.2% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4% Sum of yearly loss experiences Use in Segment 1: Current Grade A (1) 26

27 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate-Static Pool Method Charged-off loans divided by original balance Loss Experience in Year Following Origination Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Segment 1: Original Grade A (1) Base Loss Rate % 0.0% 0.2% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% % 0.0% 0.2% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% % 0.0% 0.2% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.5% % 0.0% 0.1% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.4% % 0.0% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.2% % 0.0% 0.2% 0.4% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.6% % 0.1% 0.0% 0.2% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% % 0.2% 0.5% 0.2% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 1.1% % 0.3% 0.7% 0.5% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 1.6% % 0.0% 0.1% 0.1% 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.3% Avg. 0.0% 0.0% 0.2% 0.3% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Or you could use the average loss rate in each year after origination and apply it to the unknown years (shaded cells), but then need to segment by year! Sum of yearly loss experiences Apply to Segmentation by Year 27

28 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate-Static Pool Method From Static Pool Analysis by Original Grade Balance Segment 1 Base Loss Rate Economic Adjustment Expected Loss Rate Expected Credit Loss A (1) $200,000, %.01% 0.41% $820,000 B (2) 150,000, %.01% 0.56% 840,000 C (3) 75,000, %.01% 0.91% 682,500 D (4) 25,000, %.01% 3.52% 880,000 E (5) 5,000, %.01% 10.14% 507,000 Sub-total $455,000,000 $3,729,500 Balances by Current Grade More on this later 28

29 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate-Loss Migration Method as of 12/31/15 Loss Migration-CQI History Loan ID Charge-off Date Charge-off Balance ($) Q Q Q Q Q Q Q Q /15/ D C A A A A A A /15/15 75 E C B B A A A A /15/ E D C C C A A A /15/ D C C C B B B /15/ C B B B B B B. Charge-offs not included in periods after charge off date Could use month, quarter, year Go back in time to each loan s origination date CQI = Credit Quality Indicator 29

30 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate-Static Pool Method as of 12/31/15 Loss Migration-CQI History Loan ID Charge-off Date Charge-off Balance ($) Q Q Q Q Q Q Q Q /15/ D C A A A A A A /15/15 75 E C B B A A A A /15/ E D C C C A A A /15/ D C C C B B B /15/ C B B B B B B as of 12/31/15 Loss Migration-Segment 1: Grade A Q Q Q Q Q Q Q Q Charge-offs ($) Balance 1 ($) 100,000 90,000 95, , ,000 95, , ,000. Loss Rate 2 (%) 0% 0%.42%.38%.70% 1.16% 1.10% 1.05%.. Notice only looking at Grade A 1 Beginning balance of period 2 Annualized We now have an annualized charge-off ratio for each quarter reflecting data up through 12/31/15 30

31 Step 3. Estimate the Expected Loss Rate for each Segment: Loss Rate-Loss Migration Method as of 12/31/15 Loss Migration-CQI History Loan ID Charge-off Date Charge-off Balance ($) Q Q Q Q Q Q Q Q /15/ D C A A A A A A /15/15 75 E C B B A A A A /15/ E D C C C A A A /15/ D C C C B B B /15/ C B B B B B B Notice only looking at Grade B as of 12/31/15 Loss Migration-Segment 1: Grade B Q Q Q Q Q Q Q Q Charge-offs ($) Balance 1 ($) 80,000 85,000 82,000 83,000 81,000 80,000 79,000 83,000. Loss Rate 2 (%) 0% 0%.49%.48%.12% 0.38% 0.38% 0.36%.. 1 Beginning balance of period 2 Annualized KEY POINT: Repeat for each Segment/CQI. Then derive an appropriate Loss Rate (average over certain time period, weight and average from prior analysis dates, etc.) 31

32 Step 3. Estimate the Expected Loss Rate for each Segment: PD Method 1. Probability of Default Method (PD) 2. Loss Rate Method 3. Discounted Cash Flow Method (DCF) 32

33 Step 3. Estimate the Expected Loss Rate for each Segment: DCF Method Discount Rate must be the Effective Interest Rate (per FASB) 33

34 Step 3. Estimate the Expected Loss Rate for each Segment: DCF Method Loan Amount = $100,000 Term = 180 Months Rate = 5.0% Payment = $ Discount Rate= 5.0% Path 1-Full maturity Path 2-Prepay with no loss to principal Path 3-Default (Prepay with loss to principal) Path 1: What is the Present Value if the loan goes full term? $100,000 Path 2: What is the Present Value if the loan pays off in full after just 4 years? $100,000 If present value always equals the balance (amortized cost), what is the point of doing a discounted cash flow analysis? Path 3: Timing of Defaults does matter 34

35 Step 3. Estimate the Expected Loss Rate for each Segment: DCF Method Loan Amount = $100,000 Term = 180 Months Rate = 5.0% Payment = $ Discount Rate= 5.0% Default occurs at 24 months Balance = $90,577 Net Proceeds = $75,000* PV of Payments = $17,310 PV of Net Proceeds = $68,593 PV = $85,902 Loss = $14,098 Default occurs at 48 months Balance = $80,166 Net Proceeds = $75,000* PV of Payments = $33,691 PV of Net Proceeds = $62,078 PV = $95,769 Loss = $4,231 Default occurs at 72 months Balance = $68,662 Net Proceeds = $75,000* PV of Payments = $48,516 PV of Net Proceeds = $56,182 PV = $104,699 Loss = $0 *For simplicity we assumed the net proceeds from the sale of collateral was the same in each scenario. 35

36 Step 3. Estimate the Expected Loss Rate for each Segment: DCF Method The Problem is we don t know when defaults will occur (at 24 months? 48 months? 72 months?) We need a similar analysis as the Probability of Default (each loan contributing a little bit to the overall expected loss). Conditional Prepayment Rate (CPR) Conditional Repayment = Rate (CRR) + Conditional Default Rate (CDR) or Default Proportion In a pool of loans some portion will Prepay Some portion of the Prepay will include defaults 36

37 Step 3. Estimate the Expected Loss Rate for each Segment: DCF Method (Beginning Balance Principal Payment) x (1- CPR) Adjusted to monthly rate Prepayments x Default Proportion x Loss Severity Month CPR Default Proportion Loss Severity Beginning Balance 1,000,000 Prepayments 37 Expected Loss Discounted Expected Loss Expected Loss Rate 1 10% 70% 20% 982,363 8,663 1,213 1, % 2 15% 70% 20% 964,826 8,508 1,191 1, % 3 15% 70% 20% 947,387 8,354 1, % % 70% 20% % Total 522,421 73,139 61, % CPR and default statistics can vary Used in the Allowance for over time Expected Credit Loss KEY POINT: DCF turns out to be a very similar methodology to the PD Method.

38 Step 3. Estimate the Expected Loss Rate for each Segment Base Loss Rate Economic Adjustments: Current Conditions Economic Adjustments: Reasonable & Supportable Forecasts Expected Loss Rate 38

39 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments Under an expected credit loss model, the consideration of past and current conditions may be a good starting point in estimating credit losses. However, an expected credit loss model would also require incorporating reasonable and supportable forecasts about collectability over the remaining contractual cash flows. (FASB) 39

40 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Economic Cycle) Expansion Peak Contraction Trough Interest Rates Increasing High Decreasing Low Loan Demand Increasing High Decreasing Low Deposit Growth Moderate Low Moderate High Liquidity Decreasing Low Increasing High Monetary Policy Tightening Tight Easing Ease Yield Curve Rising/Flattening Flat/Falling Reverse Rising Steeply 40

41 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Economic Response) Expansion Peak Contraction Trough Deposits/ Liabilities: Shorten Maturities Minimize Maturities Lengthen Maturities Maximize Maturities Loans: Lengthen Maturities Maximize Maturities Shorten Maturities Minimize Maturities Loan Quality: Acquire Fixed Rate Upgrade Quality Restrict Fixed Rate Restrict Fixed Rate Investments: Acquire Investments Lengthen Maturities Upgrade Quality Plan Sales Shorten Maturities Sell Investments Minimize Maturities Credit Lines: Replenish Purge 41

42 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) Which variables to include? Which variables to forecast? GDP Interest Rates (Index) No-Collinear or Insignificant Consumer Sentiment Index House Price Index (HPI) Unemployment Yes 42

43 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) How to forecast? ARIMA Model Your Own Forecast Custom Model Use Directly Industry Forecast Adjust 43

44 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) Your Own Forecast ARIMA Model House Price Index (HPI) Custom Model Use Directly Industry Forecast Adjust 44

45 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) ARIMA (Auto-Regressive Integrated Moving Average) 1 Use previous data points to predict future data points 2 Analyze correlation between an observation (x t ) and the one that precedes it (x t-1 ) 3 If correlation exists, an auto-regressive model is likely useful 45

46 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) House Prices Current values highly correlated with previous ones ( r =.9873) Forecast Using ARIMA you can forecast on the MSA level: -Affects PD -Affects LGD (forecast actual home prices)

47 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) Your Own Forecast ARIMA Model Unemployment Other Model Use Directly Industry Forecast Adjust 47

48 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) National Forecast What about MSA? 48

49 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) 49

50 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) 14 Unemployment Rates (%) Highly Correlated 2 0 1/1/2005 4/1/2005 7/1/ /1/2005 1/1/2006 4/1/2006 7/1/ /1/2006 1/1/2007 4/1/2007 7/1/ /1/2007 1/1/2008 4/1/2008 7/1/ /1/2008 1/1/2009 4/1/2009 7/1/ /1/2009 1/1/2010 4/1/2010 7/1/ /1/2010 1/1/2011 4/1/2011 7/1/ /1/2011 1/1/2012 4/1/2012 7/1/ /1/2012 1/1/2013 4/1/2013 7/1/ /1/2013 1/1/2014 4/1/2014 7/1/ /1/2014 1/1/2015 4/1/2015 7/1/2015 National Los Angeles 50

51 Step 3. Estimate the Expected Loss Rate for each Segment: Economic Adjustments (Forecasts) Base Loss Rate Economic Adjustments: Current Conditions Economic Adjustments: Reasonable & Supportable Forecasts Expected Loss Rate *Research pending PD Models: Included in PD Loss Rate: Direct Adjustments Needed DCF Models: Included in CDR* 51 Intuition-Probably not sufficient Correlation with Loss Rates From PD Model

52 Review Steps Step 1. Properly segment the portfolio Balance Residential Mortgages Expected Loss Rate Expected Credit Loss A (1) $200,000, % $560,000 B (2) 150,000, % 450,000 C (3) 75,000, % 562,500 D (4) 25,000, % 675,000 E (5) 5,000, % 337,500 Sub-total $455,000,000 $2,585,000 Consumer-Auto A (1) B (2) Step 2. Decide on an appropriate Credit Etc. Quality Indicator for each Segment 52 Step 3. Estimate an appropriate Expected Loss Rate, including economic adjustments, for each Segment Step 4. Multiply each Segment/CQI balance by its Expected Loss Rate and then sum each Segment s sub-total to calculate the Allowance for Expected Credit Loss

53 Agenda 1 Introduction 2 Calculating the Allowance for Expected Credit Loss 3 4 Required Disclosures Conclusion Notice: FASB is still deliberating and has not made a final accounting standards update! 53

54 Required Disclosures Broad Goals 1. Understand the credit risk inherent in the portfolio and how management monitors the credit quality of the portfolio. 2. Understand management s estimation of expected credit losses. 3. Understand changes in the estimate that have taken place during the reporting period. 54

55 Required Disclosures Sections -Level of Disaggregation (Segmentation) -Credit Quality Indicators -Allowance for Expected Credit Loss -Roll Forward Information -Vintage Analysis -Past Due/Nonaccrual Status -Other 55

56 Required Disclosures Level of Disaggregation-Example We segment our portfolio by the following lines of business. Include a description of each segment and sub-segment. Business/Commercial SBA Commercial Real Estate Commercial Other Consumer Credit Card Auto Other Secured Other Unsecured Residential First Mortgage Other Other 56

57 Required Disclosures Credit Quality 1 A description of the credit quality indicator per segment 2 The amortized cost by credit quality indicator 3 For each credit quality indicator the date or range of dates last updated 57

58 Required Disclosures Credit Quality-Example Other Unsecured by Segment We monitor credit quality for our other unsecured portfolio by credit score. Credit scores are updated quarterly. The scores were last updated 6/30/15. Need to disclose wh en last updated 58

59 Required Disclosures Allowance for Expected Credit Loss 1. Understand the method(s) used 2. Understand the information management used in developing its estimate of expected credit loss 3. Understand the economic assumptions that caused changes to the allowance, which affects the credit loss expense 59

60 Required Disclosures Allowance for Expected Credit Loss A description of how the expected loss estimates are developed A description of the factors that influenced management s current estimate of expected credit losses (Past events, current conditions, reasonable and supportable forecasts) A discussion about the risk characteristics of each segment A discussion about any changes in factors that influenced the expected loss A discussion of any changes in an entity s accounting policies, methodology, and/or estimation techniques An explanation of any significant changes in the amount of write-offs. 60

61 Required Disclosures Allowance for Expected Credit Loss (Roll Forward)-Example Segment: Mortgage Method: Probability of Default Sept. 30, 2015 Dec. 31, 2015Mar. 31, 2016 Beginning Balance $2,200 $2,100 $2,000 -Charge-offs $800 $700 +Recoveries $0 $100 Balance Before ECL Provision $1,400 $1,500 +Provision for Expected Credit Loss$700 $500 Ending Balance $2,100 $2,000 61

62 Required Disclosures Vintage Analysis-Example +5 Full Years +YTD +Prior Periods Credit Quality Indicator Balance ($) % of Subtotal % of Portfolio Grade A (YTD) 25,000, % 6.9% ,000, % 14.4% , % 15.0% ,000, % 11.1% ,000, % 10.6% ,000, % 2.8% All Others 15,000, % 4.2% Subtotal 234,000, % 65% 62

63 Required Disclosures Delinquency Aging 63

64 Required Disclosures Delinquency Aging-Example 64

65 Required Disclosures Other Disclosures -Reconciliation between Fair Value and Amortized Cost for Certain Debt Instruments -Purchased Credit Impaired Financial Assets -Transition Disclosures for Interim Reporting Periods -Reversion Method After Reasonable & Supportable Forecast Period -Collateral Dependent Qualitative Disclosure 65

66 Agenda 1 Introduction 2 Calculating the Allowance for Expected Credit Loss 3 4 Required Disclosures Conclusion Notice: FASB is still deliberating and has not made a final accounting standards update! 66

67 Conclusion Allowance for Expected Credit Loss: Main Objective The main objective of the Accounting Standards Update is to provide financial statement users with more decision useful information about an institution s expected credit losses by requiring consideration of a broader range of reasonable and supportable information. 67 More Discussion and Disclosures -No Probable Threshold -Past, Current, and Future

68 Conclusion Allowance for Expected Credit Loss: Balance Sheet Statement of Financial Condition (Balance Sheet) Assets Total Loans $100,000,000 Less Allowance for Expected Credit Loss <$2,000,000> Net Loans $98,000,000 Current estimate of cash flows NOT expected to be collected. 68

69 Conclusion Review steps to calculating the Allowance for Expected Credit Loss Step 1. Properly segment the portfolio Balance Residential Mortgages Expected Loss Rate Expected Credit Loss A (1) $200,000, % $560,000 B (2) 150,000, % 450,000 C (3) 75,000, % 562,500 D (4) 25,000, % 675,000 E (5) 5,000, % 337,500 Sub-total $455,000,000 $2,585,000 Consumer-Auto A (1) B (2) Step 2. Decide on an appropriate Credit Quality Indicator for each Segment 69 Step 3. Estimate an appropriate Expected Loss Rate, including economic adjustments, for each Segment [ ] Three Main Methods to Calculate Expected Loss Rate: 1. Probability of Default Method (PD) 2. Loss Rate Method (w/ Static Pool or Loss Migration) 3. Discount Cash Flow Method (DCF) Step 4. Multiply each Segment/CQI balance by its Expected Loss Rate and then sum each Segment s sub-total to calculate the Allowance for Expected Credit Loss

70 Conclusion Allowance for Expected Credit Loss From prior period Dec. 31, 2015Mar. 31, 2016 Beginning Balance $2,100 $2,000 -Charge-offs $700 +Recoveries $100 Balance Before Expected Credit Loss Provision $1,500 +Provision for Expected Credit Loss $500 Ending Balance $2,000 You first calculate the required Allowance for Expected Credit Loss Then use the Provision to balance 70

71 Conclusion Allowance for Expected Credit loss: Income Statement Statement of Financial Performance (Income Statement) Interest Income Loans $10,000,000 Etc. $ 1,000,000 Total $11,000,000 Interest Expense Deposits/Shares $ 2,000,000 Etc. $ 0 Total $ 2,000,000 Net Interest Income $ 9,000,000 Less Provision for Expected Credit Loss <$ 500,000> The provision reduces the Net Interest Income Net Interest Income After Provision for Expected Credit Loss $ 8,500,000 71

72 Conclusion Tips to a smooth transition 1 Decide on segmentation, credit quality indicator, and expected loss rate method you will use. 2 Identify data elements and analytics needed (internal and external) Run new model in parallel to existing methodology. Start building out required discussion and disclosures items. Don t panic! There will be transition time and this is easier than many are making it out to be 72

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77 Just Click Access these Resources

78 Within Checkpoint We Have Access to: 1. FASB Codifications 2. IRS Rulings 3. AICPA Standards 4. Employment / Wage Laws and Regulations

79 Searching CECL A quick search of CECL brings FASB Board decisions, news and industry discussions about the topic.

80 Searching CECL Additionally, with codifications there are legal explanations of how to read the material within and understand how changes will impact your credit union.

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