1. Modeling Impacts from Current Expected Credit Loss Framework
November 20, 2014
Presented by
Joe Feldmann
FI Consulting

2. Introduction
FASB proposal to shift from Incurred Loss to Current Expected Credit Losses (CECL) will have a range of impacts:
Financial
Accounting
Operational
Data Collection
Model Updates
Supporting Analytical Processes

3. Modeling in an Incurred Loss Framework

4. Modeling Process under FAS 5
Data Inputs
Model
Current Book
Historical Portfolio / Peer Performance
Other data observable prior to financial reporting date
Derives Segment / Cohort Level Assumptions
Estimates losses over emergence or recognition period
Loss Reserves
Management Decisions
Adjust modeled results for blind spots
Confirm or adjust key model assumptions

5. Incurred Loss Modeling Exercise
We created a basic incurred loss model for a residential portfolio. Some basic portfolio characteristics:
Freddie Mac conforming loans
Sample of 425,000 loans originated between
Fixed Rate 30 Year
Nationwide portfolio
Model variables include: Age, Delinquency Status, State
Avg. Orig. UPB
Avg. Orig. LTV
Avg. FICO
Avg. Coupon
$219,913
71.2%
750
5.08%

6. Sample Incurred Loss Model Results

7. Moving to an Expected Credit Losses Framework

8. FASB Proposal

9. FASB Proposal – Key Impacts
Three significant impacts derive from the expected credit loss framework:
– “…Therefore, a further adjustment should be made, as necessary, to reflect current information that may indicate current expectations about loss that is not reflected in the historical experience.”
– “An estimate of expected credit losses shall reflect the time value of money either explicitly or implicitly.”
– “The estimation of expected credit losses is highly judgmental…Such judgments include the following:”
“e. How expected prepayments affect the allowance for credit losses as of the reporting date”

10. Modeling Process under CECL
Data Inputs
Model
Current Book
Historical Portfolio / Peer Performance
Other data observable prior to financial reporting date
Forecasted economic indicators
Derives Segment / Cohort Level Assumptions
Guidance does not dictate model methodology, though loan-level modeling may address guidance more effectively
Estimates losses over emergence or recognition period
Estimates lifetime losses discounted to present value
Evaluates default vs. prepay decision
Loss Reserves
Management Decisions
Adjust modeled results for blind spots
Confirm or adjust key model assumptions
Supportable forecasts of economics and prepayment

11. CECL Modeling – Specific Data Impacts
New data that may be considered for inclusion in the reserve models and judgment process:
House Prices
Interest Rates
Unemployment
Income
Legal and regulatory issues
Other unique local/demographic/economic issues
Not all data needs to be modeled, but periodic collection and analysis may be appropriate

12. Economic Condition Data – House Prices
Source:

13. Economic Condition Data - House Prices
House prices are important enough to warrant consideration in the CECL framework:
Need both historical and forecasted data—with some level of consistency between the two
Housing markets are local so models needs to consider geography
Short-term forecast should reflect management judgment, though the impact of that judgment will lessen as the forecast horizon increases

14. CECL Modeling Exercise
We created an expected credit loss model for the same residential portfolio. Differences from the Incurred Loss Model to this CECL model include:
Use of econometric model
Addition of model variables:
Forecasted HP change
Forecasted changes in borrower income
OLTV
FICO

15. Sample CECL Model Results

16. Sample CECL Model Results

17. Supporting Analyses in the CECL Framework

18. Analytical Processes to Substantiate CECL Estimates
Given the significance of the changes, loss reserves are likely to face increased audit scrutiny, particularly during the transition periods.
Analytical processes that may need to be developed or enhanced include:
Support for Management’s Forecast
Scenario Analyses
Benchmarking
Model Performance Testing
Controls around data and modeling processes will continue to be important

19. Federal Government Financial Reporting for Loan Portfolios
US Federal Government has an approach analogous to CECL and may be a helpful benchmark:
The Federal Credit Reform Act of 1992 (FCRA) governs the reporting of the cost of credit programs in the federal government.
FCRA requires agencies to consider forecasted economic conditions and discount future cash flows to calculate the cost of their credit programs.
FHA has reported results under this framework since FCRA was enacted.

20. FCRA Results from FHA