Modeling Impacts from
Current Expected Credit Loss Framework
November 20, 2014
Presented by
Joe Feldmann
FI Consulting
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
Modeling in an Incurred Loss Framework
Modeling Process under FAS 5
Data Inputs
• Current Book
• Historical
Portfolio /
Peer
Performance
• Other data
observable
prior to
financial
reporting
date
Model
Management
Decisions
• Adjust
modeled
results for
blind spots
• Confirm or
adjust key
model
assumptions
• Derives
Segment /
Cohort Level
Assumptions
• Estimates
losses over
emergence
or
recognition
period
Loss Reserves
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 2006-2013
• Fixed Rate 30 Year
• Nationwide portfolio
Avg. Orig. UPB
Avg. Orig. LTV
Avg. FICO
Avg. Coupon
$219,913
71.2%
750
5.08%
• Model variables include: Age, Delinquency Status, State
Sample Incurred Loss Model Results
Moving to an Expected Credit Losses
Framework
FASB Proposal
FASB Proposal – Key Impacts
Three significant impacts derive from the expected credit loss framework:
825-15-25-3 – “…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.”
825-15-25-4 – “An estimate of expected credit losses shall reflect the
time value of money either explicitly or implicitly.”
825-15-55-2 – “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”
Modeling Process under CECL
Data Inputs
• Current Book
• Historical
Portfolio / Peer
Performance
• Other data
observable prior
to financial
reporting date
• Forecasted
economic
indicators
Model
Management
Decisions
• Adjust modeled
results for blind
spots
• Confirm or adjust
key model
assumptions
• Supportable
forecasts of
economics and
prepayment
• 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
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
Economic Condition Data –
House Prices
Source:http://www.washingtonpost.com/blogs/wonkblog/wp/2014/05/06/why-home-prices-are-reachingbubble-era-prices-without-bubble-era-headaches/#excerpt
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
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
Sample CECL Model Results
Sample CECL Model Results
Supporting Analyses in the CECL
Framework
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
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.
FCRA Results from FHA