Economic Impact Analysis for Credit Unions

In a recent webinar I participated in with SAS we discussed Economic Impact Analysis (EIA). While EIA is similar in concept to stress testing, its main goal is to allow credit unions to move quickly to evaluate economic changes to their portfolio—such as those brought about by a crisis like the COVID-19 pandemic.

There are four main components to EIA.

  1. Portfolio data: At a minimum this needs to be segment level with loss performance through time. If needed, this data could be augmented with external data
  2. Scenarios: Multiple economic possibilities are necessary to help assess timing and magnitude of potential, future loss
  3. Models or methodologies: These are required to link scenarios to the portfolio to forecast loss amounts
  4. Visualization of results: This is essential to clearly understand the portfolio loss behavior. While tables are useful, nothing illustrates odd behavior better than a picture (e.g., a box plot or tree map or histogram).

A credit union looking for a practical approach for getting started should consider the following steps:

  • Start with segment level data instead of account level. This should reduce the common complexities that arise when sourcing and cleaning account level data.
  • Develop segment level models or methodologies to capture the impacts of macroeconomic changes.  These can be simple provided they incorporate relationships to macroeconomic elements.
  • Create multiple scenarios. The more the better. Different scenarios will provide different insights in how the portfolio reacts to changing macroeconomic environments.
  • Execute models and explore results. This is where (I believe) the fun begins. Be curious – change portfolio assumptions (e.g., growth or run-off), and scenarios, to see how losses will react.

Now is the time to act, to gain an understanding about the economy’s impact on one’s portfolio. But it is worth mentioning this is also an investment into the future. As mentioned earlier, EIA has its roots in stress testing. By creating an EIA process now, a credit union not only better positions itself to build a robust stress test platform but also has the foundation to tackle CECL.

To view the webinar on demand, please visit NAFCU.

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, and technical communication. He has over 15 years’ experience in the area of financial risk.

The Financial Risk Group Is Now FRG

We’re making it official: After more than a decade of operating as “The Financial Risk Group,” we’re changing our name to reflect what our clients have called us since the early days. We are excited to formally debut our streamlined “FRG” brand and logo.

Our new look is a natural progression from where we started 14 years ago, when the three founding partners of this company set a lofty goal. We wanted to become the premier risk management consulting company. It seemed ambitious, considering we were operating out of Ron Holanek’s basement at the time, but we knew we had at least two things going for us: a solid business plan and a drive to do whatever it took to deliver success for our clients.

And look at us now. It would take a while to list everything we’ve accomplished over the last decade plus, but here’s a quick run down of some of the items we’ve crossed off the company bucket list since 2006.

  • We’ve grown our numbers from the original three to more than 50 talented risk consultants, analysts, and developers.
  • We moved out of the basement (it would have been a tight fit, considering). We settled in historic downtown Cary in 2008, but quickly spilled out of our main office there and into several satellite locations. In 2018 we bought an older building a few blocks away and renovated it to a gleaming modern office hub for our US headquarters.
  • We opened offices in Toronto, Canada and Kuala Lumpur, Malaysia, to better serve our clients around the world.
  • We opened several new business units, expanding on our original core focus of delivering automated technology solutions. Adding dedicated Data and Risk, Business Analytics, and Platform Hosting teams enlarged our wheelhouse, so that we have experts that can walk our clients through the entire lifecycle of risk management programs. (Shameless plug: you can learn more about a number of them via a series of videos that are sprinkled throughout the website). We now also work with institutional investors on innovative models and product offerings to help streamline processes and drive excess returns.
  • We formalized our NEET (New Employee Excellence Training) apprenticeship program, so we can nurture and enhance the specific blend of skills that risk management professionals need to solve real-world business challenges. The program has struck a chord with our clients, so we built a program to recruit and develop risk management talent for them, as well.

Obviously, we couldn’t have done any of this without continued trust and support from our clients. Our clientele represents a cross section of the world’s largest banking, capital markets, insurance, energy and commodity firms – stretching across continents and across industries – and we recognize that they’re some very smart people. When they talk, we listen, and what they have been saying for a few years now is that the brand we started with in 2006 should evolve with the evolution of the company.

It is natural for people to streamline words into acronyms.  In our industry, there are many, and knowing them is very important to our job.  Our clients, partners, and even our internal teams used FRG from day one, but now is the time to make it official.  By rebranding and fully embracing the FRG name, we hope that it, too, becomes a well-known acronym in the risk management space, one that people equate with integrity and quality of work.

So we’re celebrating 2020 with the new name, a new look, and a new logo. But it’s like they say. The more things change, the more they stay the same. That’s why you can be sure that our core values, our core principle – to fulfill our clients’ needs, while surpassing their expectations – still guide us every day. We are our reputation. We are FRG.

Mike Forno is a Partner and Senior Director of Sales with FRG.

 

CECL Preparation: Questions to Consider When Selecting Loss Methodologies

Paragraph 326-20-30-3 of the Financial Accounting Standards Board (FASB) standards update[1] states: “The allowance for credit losses may be determined using various methods”. I’m not sure if any statement, other than “We need to talk”, can be as fear inducing. Why is it scary? Because in the world of details and accuracy, this statement is remarkably vague and not prescriptive.

Below are some questions to consider when determining the appropriate loss methodology approaches for a given segment.

How much history do you have?

If a financial institution (FI) has limited history[2] then the options available to them are, well, limited. To build a model one needs sufficient data to capture the behavior (e.g., performance or payment) of accounts. Without enough data the probability of successfully building a model is low. Worse yet, even if one builds a model, the likelihood of it being useful and robust is minimal. As a result, loss methodology approaches that do not need a lot of data should be considered (e.g., discount cashflow or a qualitative factor approach based on industry information).

Have relevant business definitions been created?

The loss component approach (decomposing loss into PD, LGD, and EAD) is considered a leading practice at banks[3]. However, in order to use this approach definitions of default and, arguably, paid-in-full, need to be created for each segment being modeled. (Note: these definitions can be the same or different across segments.) Without these definitions, one does not know when an account has defaulted or paid-off.

Is there a sufficient number of losses or defaults in the data?

Many of the loss methodologies available for consideration (e.g., loss component or vintage loss rates) require enough losses to discern a pattern. As a result, banks that are blessed with infrequent losses can feel cursed when they try to implement one of those approaches. While low losses do not necessarily rule out these approaches, it does make for a more challenging process.

Are loan level attributes available, accurate, and updated appropriately?

This question tackles the granularity of an approach instead of an approach itself. As mentioned in the post CECL – Data (As Usual) Drives Everything, there are three different data granularity levels a model can be built on. Typically, the decision is between loan-level versus segment level. Loan-level models are great for capturing sensitivities to loan characteristics and macroeconomic events provided the loan characteristics are accurate and updated (if needed) on a regular interval.

Jonathan Leonardelli, FRM, Director of Business Analytics for the Financial Risk Group, leads the group responsible for model development, data science, documentation, testing, and training. He has over 15 years’ experience in the area of financial risk.

 

[1]FASB accounting standards update can be found here

[2] There is no consistent rule, at least that I’m aware of, that defines “limited history”. That said, we typically look for clean data reaching back through an economic cycle.

[3] See: Capital Planning at Large Bank Holding Companies: Supervisory Expectations and Range of Current Practice August 2013

RELATED:

CECL—The Caterpillar to Butterfly Evolution of Data for Model Development

CECLData (As Usual) Drives Everything

IFRS 9: Evaluating Changes in Credit Risk

Determining whether an unimpaired asset’s credit risk has meaningfully increased since the asset was initially recognized is one of the most consequential issues banks encounter in complying with IFRS 9. Recall the stakes:

  • The expected credit loss for Stage 1 assets is calculated using the 12-month PD
  • The ECL for Stage 2 assets (defined as assets whose credit risk has significantly increased since they were first recognized on the bank’s books) is calculated using the lifetime PD, just as it is for Stage 3 assets (which are in default).

To make the difference more concrete, consider the following:

  • A bank extends an interest-bearing five-year loan of $1 million to Richmond Tool, a hypothetical Virginia-based tool, die, and mold maker serving the defense industry.
  • At origination, the lender estimates the PD for the next 12 months at 1.5%, the PD for the rest of the loan term at 4%, and the loss that would result from default at $750,000.
  • In a subsequent reporting period, the bank updates those figures to 2.5%, 7.3%, and $675,000, respectively.

If the loan were still considered a Stage 1 asset at the later reporting date, the ECL would be $16,875. But if it is deemed a Stage 2 or Stage 3 asset, then the ECL is $66,150, nearly four times as great.

Judging whether the credit risk underlying those PDs has materially increased is obviously important. But it is also difficult. There is a “rebuttable presumption” that an asset’s credit risk has increased materially when contractual payments are more than 30 days past due. In general, however, the bank cannot rely solely upon past-due information if forward-looking information is to be had, either on a loan-specific or a more general basis, without unwarranted trouble or expense.

The bank need not undertake an exhaustive search for information, but it should certainly take into account pertinent intelligence that is routinely gathered in the ordinary course of business.

For instance, Richmond Tool’s financial statements are readily available. Balance sheets are prepared as of a point in time; income and cash flow statements reflect periods that have already ended. Nonetheless, traditional ratio analysis serves to evaluate the company’s prospects as well as its current capital structure and historical operating results. With sufficient data, models can be built to forecast these ratios over the remaining life of the loan. Richmond Tool’s projected financial position and earning power can then be used to predict stage transitions.

Pertinent external information can also be gathered without undue cost or effort. For example, actual and expected changes in the general level of interest rates, mid-Atlantic unemployment, and defense spending are likely to affect Richmond Tool’s business prospects, and, therefore, the credit risk of the outstanding loan. The same holds true for regulatory and technological developments that affect the company’s operating environment or competitive position.

Finally, the combination of qualitative information and non-statistical quantitative information such as actual financial ratios may be enough to reach a conclusion. Often, however, it is appropriate to apply statistical models and internal credit rating processes, or to base the evaluation on both kinds of information. In addition to designing, populating, and testing mathematical models, FRG can help you integrate the statistical and non-statistical approaches into your IFRS 9 platform.

For more information about FRG’s modeling expertise, please click here.

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