Choosing a CECL Methodology | Doable, Defensible, Choices Amid the Clutter

CECL advice is hitting financial practitioners from all sides. As an industry friend put it, “Now even my dentist has a CECL solution.” With many high-level commentaries on CECL methodologies in publication (including RiskSpan’s ), we introduce this specific framework to help practitioners eliminate ill-fitting methodologies until one remains per segment. We focus on the commercially…

CECL: DCF vs. Non-DCF Allowance — Myth and Reality

FASB’s CECL standard allows institutions to calculate their allowance for credit losses as either “the difference between the amortized cost basis and the present value of the expected cash flows” (ASC 326-20-30-4) or “expected credit losses of the amortized cost basis” (ASC 326-20-30-5). The first approach is commonly called the discounted cash flow or “DCF…

cecl dcf non-dcf

What Data Do I Need For CECL Modeling?

Even with CECL compelling banks to collect more internal loan data, we continue to emphasize profitability as the primary benefit of robust, proprietary, loan-level data. Make no mistake, the data template we outline below is for CECL modeling. CECL compliance, however, is a prerequisite to profitability. Also, while third-party data may suffice for some components of the CECL estimate, especially in the early years of implementation, reliance on third-party data can drag down profitability. Third-party data is often expensive to buy, may be unsatisfactory to an auditor, and can yield less accurate forecasts. Inaccurate forecasts mean volatile loss reserves and excessive capital buffers that dilute shareholder profitability. An accurate forecast built on internal data not only solves these problems but can also be leveraged to optimize loan screening and loan pricing decisions.

Sample Size Requirements for CECL Modeling

With CECL implementation looming, many bankers are questioning whether they have enough internal loan data for CECL modeling. Ensuring your data is sufficient is a critical first step in meeting the CECL requirements, as you will need to find and obtain relevant third-party data if it isn’t. This article explains in plain English how to calculate statistically sufficient sample sizes to determine whether third-party data is required. More importantly, it shows modeling techniques that reduce the required sample size. Investing in the right modeling approach could ultimately save you the time and expense of obtaining third-party data.

What CECL Means To Investors

Recent updates to U.S. GAAP will dramatically change the way financial institutions incorporate credit risk into their financial statements. The new method is called the Current Expected Credit Loss (CECL) model and will take effect over the next few years. For many institutions, CECL will mean a one-time reduction in book equity and lower stated earnings during periods of portfolio growth. These reductions occur because CECL implicitly double-counts credit risk from the time of loan origination, as we will meticulously demonstrate. But for investors, will the accounting change alter the value of your shares?

What to Look for in a Current Expected Credit Loss (CECL) Partner

Financial institutions can partner with consulting firms to get ahead of the new CECL standard. A comprehensive CECL solution requires expertise from a wide range of disciplines, including data management, econometric and credit risk modeling, accounting, and model risk governance. Different financial institutions will need more outside resources in some of these areas than others. The ideal one-stop CECL partner, therefore, will have a breadth and depth of expertise such that your financial institution can trust substantial CECL-related work to them, but enough modularity in their offering so that you only pay for the services you need.


In 2012, economists from the FHFA published a research paper describing a countercyclical approach for estimating the capital level required for mortgage portfolios to withstand future shocks to the housing sector. This approach uses state-level countercyclical stressed housing price paths (CSPs) based on where a state’s housing price levels are relative to its long-run trends and on the historical downside volatility of the state’s housing prices. FHFA has published a set of 51 of these 30-year CSPs (one for each state plus DC) starting from 13 different launch dates (each of the past 7 quarters—Q4 2013 through Q2 2015—as well as 6 quarters from 2003 to 2010).[1] While the approach is not new, we believe it provides an interesting alternative to the Federal Reserve’s annual Dodd Frank Act Stress Test (DFAST) stressed housing price scenarios because it is more transparent and more granular. This paper compares FHFA’s CSPs to the DFAST stressed HPI scenarios and outlines an approach for applying the state-level granularity of the CSPs to national-level DFAST Severely Adverse scenario.