Preparing for a Current Expected Credit Loss (CECL) Framework
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Discussion
Session Overview
▪ Background
▪ CECL Details
▪ Timeline for Implementation
▪ Methodologies to Accommodate CECL
▪ Example of Balance Sheet Impact
▪ Questions
▪ Contact Information
Jonathan B. Glowacki, FSA, CERA, MAAA
Principal and Consulting Actuary, Milliman
Jonathan holds a Bachelor of Science degree in Mathematics and isa Fellow of the Society of Actuaries, a Chartered Enterprise RiskAnalyst through the Society of Actuaries, and a Member of theAmerican Academy of Actuaries. He has provided consultingservices, including predictive analysis and econometric modeling,for mortgage servicers and investors, financial guaranty insurers,mortgage insurers, and government agencies. He has extensiveexperience in analyzing mortgage risk and mortgage-backedsecurities including evaluating loan repurchase risk, designingquality control processes, and estimating loan loss reserves.
Jonathan has published articles and presented for organizationssuch as the MBA, PRMIA, and the Society of Actuaries. Jonathanhas been involved in mortgage reform discussions with theDepartment of Treasury, FHA, FHFA, USMI, and others.
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Why CECL?To provide financial statement users with better information about expected credit loss on assets.
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Background
Current Approach for loan loss reserving:
▪ Recognizes impairment from an “incurred loss” or “probable loss”;
▪ Losses from impaired assets can only be recognized after “qualifying event”;
✓e.g., a mortgage have missed a certain number of payments;
▪ The models do not include a forecasting component to estimate future impairments on assets before they reach delinquency;
▪ Investors are seeking transparency of management’s expectation of ultimate loss;
▪ Regulators ensuring that allowance for loan and lease losses (ALLL) is a fair representation of losses expected in a portfolio; and
▪ Bankers seeking to minimize cost and reduce complexity.
▪ Assume a $100 loan that is performing (current UPB = $100);
▪ Current approach delays the recognition of credit loss until the loss is considered “probable”:
▪ Only includes past events and current conditions in estimating “probable” credit losses:
Current Methodology
Current Framework
FICO 500
LTV 105
Credit Loss Reserve $0
Current Framework
Current Home Price Index 100
Estimated Future HPI 50
Credit Loss Reserve $0
Reflects zero
required reserve
until probable
loss
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Why Introduce CECL?
▪ Main objective is to expedite the recognition of credit losses of a portfolio to reflect the amount expected to be collected;
▪ Under CECL, banks would be required to develop a lifetime credit loss estimate at origination:
✓Offsets lifetime credit losses of the portfolio;
✓Reserve buffer is built up over the first year as loans are originated;
✓Reflects past event, current conditions, and reasonable & supportable forecasts; and
✓Requires an allowance on HTM securities;
▪ Investment community generally approves the CECL framework because of the increase in information about the “true reserve”; and
▪ Allowance for Credit Loss = Amortized Code and Net Amount Expected to be Collected.
▪ Assume a $100 loan that is performing;
▪ Credit loss is equal to current expected lifetime credit loss for the exposure:
▪ Includes past, current, and forecasted economic information in setting credit loss expectation:
CECL Example
Current Framework
FICO 500
LTV 105
Credit Loss Reserve $15
Current Framework
Current Home Price Index 100
Estimated Future HPI 50
Credit Loss Reserve $25
Reflects expected credit loss given risk profile of borrower
Reflects expected credit loss
given economic forecast
Details;And more details…
CECL Details▪ A front-loading of the recognition
of credit losses;
▪ Expected credit losses are defined as “current estimate of all contractual cash flows notexpected to be collected;
▪ There are no pre-defined triggers or thresholds for recognition of a credit loss;
▪ Excluded from scope:
✓ Equity instruments;
✓ Equity method investments;
✓ Derivatives;
✓ Related party loans; and
✓ Receivables between entities under common control.
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Day 1 Reserve allocation for debit instruments, loanCommitments, financial guarantees and other receivablesAnd HTM securities
CECL Details
Portfolio Segment
Credit Quality Methodology
Segmentation by Past Due
StatusOthers
DISCLOSURE REQUIREMENTSFactors making up the estimate from one period to the next
CECL Details
Technology and
Methodologies
CECL will require financial institutions to update software and modeling methodologies
Data
CECL will require financial institutions to access historical
data
Reserves
CECL will increase the amount of reserves booked by financial institutions for credit exposure (OCC estimates loan loss reserves will increase by 30 to 50 percent)
IMPACT ON FINANCIAL SERVICES INDUSTRY
SEC Filer
Dec 2019
Public Business Entities not SEC Filers
Dec 2020
All others
Dec 2021
Timeline for Implementation
Source: FASB Votes To Proceed with CECL and Delays Effective Dates by One Year, April 27, 2016, Crowe Horwath
Note: requirement is to report a CECL-based beginning balance as of the start of the year and ending as of the end of the year
IT Analysis of Systems and Requirements
Collect Historical Data
Create methodology
Auditor review and signoff
Testing for Balance Sheet
Impact
Timeline for Implementation
Independent Validation
3- 6 months 6 – 9 months 3 months
Source: FASB Votes To Proceed with CECL and Delays Effective Dates by One Year, April 27, 2016, Crowe Horwath
Working backwards, financial institutions need to start preparing 18 months before implementation!
MethodologiesWays to accommodate CECL
Methodologies to Accommodate CECL
CECL DATA REQUIREMENTS
Economic
•Historical and forecast
Current
•Loan status
•Underwriting
Historical
•Performance data
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Methodologies to Accommodate CECL
▪ There is recognition that the process to estimate lifetime credit losses is highly judgmental;
▪ The intent of the requirement is to provide a methodological process to estimate the exposure that is consistent across exposures and time;
▪ Requirements:
✓Based on internally and externally available information;
✓Shall include quantitative and qualitative factors specific to the borrowers credit quality and loan characteristics and the current economic environment;
✓Shall reflect the time value of money, discounted; and
✓ Is not a “base case” scenario but reflects some risk of loss, even if the probability is remote.
Source: Financial Accounting Series Exposure Draft: Proposed Accounting Standards Update Issued December 20, 2012
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Methodologies to Accommodate CECL
CECL Reserving is Similar to Insurance Reserving
▪ “Lifetime loss estimate” can be considered synonymous to the estimate used in traditional insurance for ultimate losses on a book of insurance liabilities;
▪ Insuring organization holds reserve for “Unpaid Claim Liabilities”:
✓Unpaid Claim Liabilities =
1. Case Reserves (equates to reserves held under current credit impairment models) plus;
2. Incurred But Not Reported “IBNR” (probable losses which would be recognized under CECL’s new credit loss accounting standards) which consist of:
✓ IBNR is the estimate calculated by an actuary:
➢ Actuarial methods are used to estimate future liabilities from past experience also accounting for changes in exposure characteristics, economic trends, and discounting.
Model Input
Origination Year Effects
Current Status / Days Past Due
Credit Quality of Borrower
Loan Product Features
Economic Information
Methodologies to Accommodate CECL
Expected Credit Loss
▪ PD / LGD Approach (similar to provision approach but can be more granular as it segments frequency and severity)
Methodologies to Accommodate CECL
FICO
Probability of
Default
A
Loss Given
Default
B
Outstanding
Loan Amount
C
Provision for
Credit Losses
D = A * B * C
350-599 15% 20% 7,500 225
600-649 10% 20% 10,000 200
650-699 6% 20% 15,000 180
700-749 5% 20% 37,500 375
750+ 2% 20% 30,000 120
Total 100,000 1,100
▪ How do you create PD / LGD factors?
✓Based on historical data (either internal or industry data);
✓Specific to asset sector;
✓Should reflect economic expectations for reasonable forecast period;
✓Can include further segmentation by credit quality of the exposure:
➢FICO;
➢Loan-to-value ratio;
➢Loan purpose; and
➢Etc.
✓Be careful of segmenting data too thin!
Methodologies to Accommodate CECL
▪ Example of Economic Impact;
✓Forecast decline house prices for next year:
Methodologies to Accommodate CECL
FICO
Probability of
Default
A
Loss Given
Default
B
Outstanding
Loan Amount
C
Provision for
Credit Losses
D = A * B * C
350-599 15% +5% 20% +5% 7,500 300
600-649 10% +4% 20% +5% 10,000 280
650-699 6% +3% 20% +5% 15,000 270
700-749 5% +2% 20% +5% 37,500 525
750+ 2% +1% 20% +5% 30,000 180
Total 100,000 1,555Based on historical averages
▪ Loss Curve / Actuarial Methodologies (vintage approach):
Methodologies to Accommodate CECL
Number of Defaults
Months of Development
Year 12 24 36 48 60 72
2008 550 1,650 3,305 5,000 5,940 6,500
2009 600 1,785 3,595 5,415 6,455
2010 720 2,165 4,315 6,485
2011 800 2,370 4,825
2012 815 2,475
2013 900
Loss Development Factors
Year 12-24 24 - 36 36 - 48 48 - 60 60 - 72 72 – Ult.
2008 3.000 2.003 1.513 1.188 1.094
2009 2.975 2.014 1.506 1.192
2010 3.007 1.993 1.503
2011 2.963 2.036
2012 3.037
2013
LDF 12-24 24 - 36 36 - 48 48 - 60 60 - 72 Tail
Average 3.00 2.01 1.51 1.19 1.09 1.08
Cum. LDF 12.71 4.24 2.11 1.40 1.18 1.08
Loss Curve 8% 24% 47% 71% 85% 93%
▪ Loss Curve / Actuarial Methodologies:
Methodologies to Accommodate CECL
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
12-24 24 - 36 36 - 48 48 - 60 60 - 72 Tail
Perc
ent
of
Defa
ults
Months of Development
Loss Curve
▪ Loss Curve / Actuarial Methodologies:
Methodologies to Accommodate CECL
Origination
Year
Original Loan
Amount
Default Rate
to-date Loss Curve
Ultimate
Default Rate
Future
Default Rate
Future
Defaults Severity Credit Loss
A B C D = B / C E = D – B F = E * A G H = F * G
2010 16,667 8.0% 93.0% 8.6% 0.6% 100 25% 25
2011 16,667 7.0% 85.0% 8.2% 1.2% 205 25% 51
2012 16,667 6.0% 71.5% 8.4% 2.4% 399 25% 99
2013 16,667 4.0% 47.4% 8.4% 4.4% 739 25% 184
2014 16,667 2.0% 23.6% 8.5% 6.5% 1,080 25% 270
2015 16,667 0.7% 7.9% 8.9% 8.2% 1,366 25% 341
Total 100,000 3,890 973
▪ Actuarial Triangles and Reports
✓Evaluate loan performance by vintage year to evaluate vintage curves with faster development compared to averages
✓Identify long-term trends from the data or changes with respect to timing patterns
✓Produce a long-term idea of loan profitability
Methodologies to Accommodate CECL
Methodologies to Accommodate CECL
Identify long-term trends from the data or changes with respect to timing patterns
Methodologies to Accommodate CECL
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
12-24 24-26 36-48 48-60 60-72 tail
Perc
ent
of
Defa
ults
Months of Development
Loss Curve
2005-2010 2010-2015
Shows that losses are developing
more quickly than historical
losses
Produce a long-term idea of loan profitability
Methodologies to Accommodate CECL
▪ Regression
▪ Analyze historical data to develop loan-level model to estimate future default risk
▪ Based on industry or internal data
▪ Explicitly can use economic assumptions as input into model estimates
Methodologies to Accommodate CECLEverbank
A Priori Model Demonstration
Default Rate Indications on Logit Transformation
Loan Attributes A Priori Calculation
FICO 600
LTV 90%
HPA -10%
A Baseline Loss Rate 8.7%
Logit Transformation (2.35)
Underwriting Risk Factor Development
B Property Type SFR 1.00
C Amortization Type FIXED 1.00
D Loan Purpose RATE/TERM 1.15
E Documentation FULL 1.00
F Interest Only / Negam NO 1.00
G Term 360 1.00
H Occupancy Investor 1.75
I Jumbo No 1.00
J Product of Underwriting Risk Factor Adjustments 2.01
Logit Transformation (risk factors) 0.70
K Underwriting Adjusted Loss Rate 16.1%
Logit Transformation (HPA) 0.98
L Econ. Adjusted Loss Rate 20.3%
Logit Transformation (HPA + Risk Factors) (0.67)
M U/W and Econ. Adjusted Loss Rate 33.8%
Example of Balance Sheet Impact
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
1.80%
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Res
erv
e A
mo
un
t (b
ps
)
Calendar Month
Comparison of CECL to Probable Loss Reserve2005 Originations
Source: Milliman Analysis and Freddie Mac Single Family Loan Level Data
CECL Reserve Probable Loss Reserve
CECL reserve builds up quickly and absorbs future reserve changes
Probable loss reserve "reacts" to worseningcredit environment
“We must make the best of those ills which cannot be avoided”—Clarence Day