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 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

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 Model variables include: Age, Delinquency Status, State Avg. Orig. UPB Avg. Orig. LTV Avg. FICO Avg. Coupon $219,913 71.2% 750 5.08%

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 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

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-reaching-bubble-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