CECL and CRE: Current Concerns, Common Characteristics, Related Solutions

By Oleg A. Blokhin, David W. Keever, and Jeffrey R. Schmidt
| 4/28/2020
CECL and CRE: Current Concerns, Common Characteristics, Related Solutions

Two top-of-mind credit-related concerns for today’s banks share one important characteristic: Both are highly data-dependent.

The two issues – adapting to the new current expected credit loss (CECL) model for estimating credit losses and complying with new stress-testing requirements for commercial real estate (CRE) portfolios – require banks to be capable of processing extensive volumes of data from a variety of sources. Moreover, a bank’s ability to successfully accomplish these critical credit initiatives is directly affected by the quality and reliability of that data.

Fortunately, this common data dependence means both functions also can be directly improved through the application of effective data management and data governance practices. Advanced data automation can have a significant positive impact on a bank’s ability to address both the data quality and data quantity challenges that are associated with CECL adoption and CRE stress testing.

Portfolio knowledge

High-profile topical issues such as the coronavirus outbreak and the resulting concerns about interest rate uncertainty and liquidity issues are generating the most immediate attention and anxiety across the financial services industry today. In the longer term, however, these top-of-mind concerns must be viewed in the context of larger, overall industry trends, such as the steady, continuing improvements in net operating revenues and reserve coverage ratios that have characterized the past decade.

It also is important to remember that the apprehensions reflected in today’s headlines make sound credit management an even more important concern. After all, most major downturns over the past 40 years – from the savings-and-loan crisis of the 1980s and 1990s to the subprime mortgage crisis of 2007-2008 – were triggered by credit issues.

The stated intentions of regulatory agencies add further impetus to credit management and credit data initiatives. Current exam priorities, as reflected in Federal Deposit Insurance Corporation and other supervisory publications, indicate that examiners are likely to pay increased attention to issues related to IT and data risk, credit risk, and internal controls, with particular focus on credit concentrations in commercial real estate and the agriculture and energy industries.

Against this background, the industry’s attention to issues related to credit data is both prudent and timely – and the sense of urgency is growing. For example, when a large group of bank executives participating in a recent Crowe webinar was asked to rank their credit departments’ critical current priorities, three out of 10 (30.1%) cited CECL implementation as their top concern.

Exhibit 1: Credit department priorities

Exhibit 1
Source: Online survey of Crowe webinar participants, Feb. 19, 2020

It also is noteworthy that an even larger portion of respondents (38.7%) cited other data-related projects such as digitizing and aggregating data as their top priorities. Moreover, in responding to another question, nearly half (49.4%) said they planned to complete these projects within the current calendar year. Clearly, gaining deeper and timely understanding of the credit portfolio is an urgent priority for many banks today.

Common data elements

CECL implementation and CRE stress testing obviously are separate and distinct activities under the broader umbrella of portfolio management. Nevertheless, much of the data required to perform these functions is drawn from common sources, and many of the necessary data management practices are comparable.

For example, the transition to the CECL methodology requires banks to identify and analyze various broad credit portfolio characteristics such as size, age, vintage, collateral, geographic location, and effective interest rate, as well as specific loan structure information such as amortization schedule, stated maturity, and repayment terms. Financial modeling tools used to perform these analyses must be calibrated to reflect each portfolio’s specific characteristics.

In addition to these internal data categories, banks also need to apply external, macroeconomic data as part of their CECL-related financial modeling. Regulatory authorities provide some of this data directly, but banks also need to perform their own analysis to identify the primary economic factors that are most relevant to their situations, and then define the specific relationships between these factors and their own portfolios.

Stress testing of a bank’s CRE portfolio involves analysis of many of the same data categories and sources including loan types, structures, and features, as well as borrower characteristics and macroeconomic factors that directly affect risk. In addition, granular segmentation of the CRE portfolio is required to perform the necessary financial modeling.

Based on their preliminary risk assessments and portfolio analysis, banks should conduct a comprehensive data inventory to assess the current state of their capabilities and identify ancillary sources that might be needed. Among the more common issues that have been observed are missing structural elements or identifiers, amortization structures that are not readily displayed, periodic data purges that make it impossible to perform necessary life-of-loan analyses, and siloed or disconnected data sources for specific loan data.

Lending data challenges

The processes involved in accessing, integrating, and validating credit portfolio data present several predictable challenges, which many banks struggle to address. Simply acquiring the relevant data often is a laborious process requiring frequent follow-ups.

Time-consuming and error-prone hand-keying of data is still common, and data standardization is often inconsistent both across various data sources and within the same sources over time. These inconsistencies might greatly impair the ability to analyze trends with accuracy. Limited traceability, due to failure or inability to retain links to original source documents, complicates data quality reviews and compliance audits.

Although such challenges are widely recognized and anticipated, a significant number of banks still report difficulty in preparing for the data needs associated with CRE stress testing and CECL implementation. In the recent Crowe webinar mentioned earlier, only 17% of the participating bank executives reported their institutions were “very ready” to meet those data needs. “Very ready” was defined as having data that is available, accessible, and usable without concern.

Exhibit 2: Credit department data preparedness

Exhibit 2
Source: Online survey of Crowe webinar participants, Feb. 19, 2020

More than half (52.3%) of the survey respondents said they were only “somewhat ready,” which was defined as having data that was generally available and accessible, but which requires effort to clean up periodically to be usable. Significantly, three out of 10 (30.7%) said they were “barely ready” or worse, engaging in many manual workarounds to make use of their data, and having to react frequently to data quality concerns.

Data automation and credit portfolio management

CECL implementation and CRE stress testing share many common data challenges and sources. They also share some common solutions, particularly in terms of the benefits that data automation can offer.

The most obvious of these benefits is the ability to reduce or outright eliminate hand-keying of data for use in financial modeling. In many instances, this process can begin by establishing an online portal that serves as a single point of contact for the customer. This secured access provides real-time visibility into the status of required customer submissions and other relevant data that can be fed automatically into both CECL and stress-testing platforms.

Advanced data automation technology also can convert unstructured information into digital data that can be used by financial modeling software. In addition to enabling transformational gains in efficiency, such systems can produce dramatic improvements in data quality while also simplifying financial reporting and audit processes.

In the current regulatory and economic environment in which accurate, real-time credit portfolio data plays a critical role in supporting performance improvement, risk management, financial reporting, and regulatory compliance initiatives, the most successful banks will be those that have implemented advanced data automation capabilities, within the broader context of effective data management and data governance policies and practices. As economic uncertainty increases, the urgency of this imperative will continue to grow.

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Oleg Blokhin
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Dave Keever
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Jeff Schmidt
Jeff Schmidt
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