preparing for a current expected credit loss (cecl) framework - … · 2019-08-30 · quality...

Preparing for a Current Expected Credit Loss (CECL) Framework

2

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.

4

Why CECL?To provide financial statement users with better information about expected credit loss on assets.

5

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


Reflects zero

required reserve

until probable

loss

7

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


Current Framework

Current Home Price Index 100

Estimated Future HPI 50


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.

10

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

17


▪ 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

18


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


Expected Credit Loss

▪ PD / LGD Approach (similar to provision approach but can be more granular as it segments frequency and severity)


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!


▪ Example of Economic Impact;

✓Forecast decline house prices for next year:


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):


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:


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


Loss Curve

▪ Loss Curve / Actuarial Methodologies:


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


Identify long-term trends from the data or changes with respect to timing patterns


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


Loss Curve

2005-2010 2010-2015

Shows that losses are developing

more quickly than historical

losses

Produce a long-term idea of loan profitability


▪ 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%

1/1

/20

05

4/1

/20

05

7/1

/20

05

10/1

/20

05

1/1

/20

06

4/1

/20

06

7/1

/20

06

10/1

/20

06

1/1

/20

07

4/1

/20

07

7/1

/20

07

10/1

/20

07

1/1

/20

08

4/1

/20

08

7/1

/20

08

10/1

/20

08

1/1

/20

09

4/1

/20

09

7/1

/20

09

10/1

/20

09

1/1

/20

10

4/1

/20

10

7/1

/20

10

10/1

/20

10

1/1

/20

11

4/1

/20

11

7/1

/20

11

10/1

/20

11

1/1

/20

12

4/1

/20

12

7/1

/20

12

10/1

/20

12

1/1

/20

13

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

Jonathan Glowacki

Thank you

[email protected]

preparing for a current expected credit loss (cecl) framework - … · 2019-08-30 · quality...

Documents