RiskSpan Director David Andrukonis Featured on The Purposeful Banker Podcast

RiskSpan’s CECL Soution Director David Andrukonis was a featured guest on PrecisionLender’s podcast, The Purposeful Banker in their recent episode titled “Is your Bank Ready for CECL” David summarized the major takeaways from a recent CECL conference, including regulator signals of forthcoming capital relief and emerging practices around reasonable and supportable forecast period length (16:19);…

Choosing a CECL Methodology

CECL presents institutions with a vast array of choices when it comes to CECL loss estimation methodologies. It can seem a daunting challenge to winnow down the list of possible methods. Institutions must consider considering competing concerns – including soundness and auditability, cost and feasibility, and the value of model reusability. Institutions must convince not…

Recent FASB Updates Related to CECL

Implementing CECL has brought about a host of accounting and other technical questions. The Financial Accounting Standards Board (FASB) works with the industry through a series of meetings to identify these questions, evaluate industry feedback, and periodically issue clarifying statements. We will continuously publish summarized points of interest from these meetings as they arise.

Basel III Capital Requirements and CECL

With the upcoming implementation of IFRS 9 in 2018, the discussion of Basel III capital requirements is gaining importance. The relationship between capital and provisions for loan-loss has been a topic of discussion as the world moves towards mandating loss provisioning by looking out over the life of a financial asset. How will this new credit-loss approach for provisioning affect regulatory capital?

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?