What’s The Same – And What’s Not – In Assessing Credit Quality

July 30th, 2018

assessment-7-30-18.pngSince the 1970s, there has been an inevitable march toward a macro, quantitative assessment of credit quality. Technology and big data ensured its emergence to complement the more traditional, transactional counterpart of credit risk management.

Since the adoption of the 2006 allowance for loan and lease losses (ALLL) guidance, and the ferocity of loan losses during the great recession, we have seen the growing confluence among credit, accounting, regulatory and investor constituencies attempting to answer the same age-old questions: How much loss is embedded in the loan portfolio? How much is this portfolio worth?

While having comparable goals, each level of management has its priorities, biases and specialized methodologies for answering those questions. For directors, there may be a need to connect the dots to determine the objective of these measures.

Today’s ALLL
The current loss methodology was also used in 2006, prior to the massive, mainly real estate, credit losses from the great recession. The 2006 methodology included pool, formula-driven and specific impairment loss estimates. The incurred loss bias of the current methodology–often known as a “run-rate” approach–inflates the most recent credit quality performances. With no significant losses prior to the crisis, the industry was largely pushed into the abyss with low loss reserves–unable to raise reserves for forecasted losses. Given the relatively benign state of credit currently, it could be said that we are back to the future, having to defend ALLL levels, largely with qualitative justifications.

Tomorrow’s CECL
The soon-to-be implemented current expected credit loss (CECL) methodology is the inevitable reaction to the roller coaster nature of today’s ALLL. Some even consider it a fall back to the failed bid, about eight years ago, to impose mark-to-market valuations on the entirety of banks’ loan portfolios. Regardless of the pejorative “crystal ball” moniker often describing CECL–not to mention estimates of significant Day One implementation increases in reserves–its integration of historical losses, current conditions and reasonable forecasts is designed to be the more holistic, life-of-loan estimation of losses.

There is a high presumption in CECL that quantitative measures, such as discounted cash flows or probabilities of default (PDs)/loss given defaults (LGDs), overlaid by recovery lags, will be used to project future losses. In theory, it may be a more reliable estimate than the current guidance; however, its greatest hindrance is the perception that it is yet another de facto variant layer of capital buffer mandated by the Dodd-Frank Act, and Basel III.

Exit Price Notion
This accounting-based fair value measure disclosure (ASU 2016-01), often referred to as fair value/exit pricing, is new for 2018 and specifies the method by which public financial institutions calculate the fair value of their loan portfolios for purposes of disclosure. Fair value is the amount that would be received to sell an asset or paid to transfer a liability at the measure date. The estimate of fair value must be supported through specified protocols of valuation and calculation. Credit-based assessments, coupled with ties to loan review and risk grade migrations, will be key to justifying a reasonable, point-in-time fair value calculation.

Credit Mark in Mergers & Acquisitions (M&A)
Speaking of fair value, in M&A, it is truly in the eye of the beholder. How skeptical is the buyer? How much does the buyer want the deal? Determining a credit mark, or rational estimate (or range) of discounts to be applied to a prospective purchased loan portfolio, is very much a credit-based, symbiotic marriage between a traditional, more qualitative loan review and the more quantitative metrics of PDs, LGDs, risk grade migrations, yield marks, recovery lags and probabilistic modeling. Using one approach, without the informing nature of the other, is problematic and increases inaccuracies. What is sacrosanct in credit mark, is that an institution never wants to undershoot the estimates. Accounting plays a greater role when the deal-negotiated credit mark is refreshed at the deal’s completion, known as Day One accounting.

The credit discipline has often described as a qualitative decision stacked on an array of quantitative metrics. That remains an apt description for transactional credit–where it all begins. However, the new frontier in managing credit risk, even at smaller financial institutions, is in the ever-evolving, mostly mandated, macro, quantitative measures–some of which are described above. Each of these, not unlike a Venn diagram, has similarities and overlapping portions, but each has separate purposes, as well. Directors, like credit officers, need to understand and embrace these quantitative measures, which will, in turn, lead to better decision making for the bank.


David Ruffin is a director at Dixon Hughes Goodman, LLP (DHG). Prior to DHG, Mr. Ruffin was co-founder and chief strategy officer of Credit Risk Management Analytics, L.L.C. Credit Risk Management was founded in 1989, and in 2015, it merged with Upland Analytics.