“You can’t outrun bad credit!” – is a well-known saying among veterans of the equipment finance industry. As a result, some veterans treat rising credit risk like a coal miner reacts to gas leaks. But it became clear to regulators and investors that not all lenders were run by seasoned veterans when over 450 financial institutions failed during the 2009 to 2012 period. One of the fallouts from the financial crisis was that reliance on existing historical-based reserve calculations led to the booking of insufficient reserves in a timely manner. As a result, many banks and financial institutions experienced heavy losses that were not anticipated to avoid a failure or surprise. The Allowance for Loan and Lease Losses (ALLL) method was called the INCURRED LOSS MODEL – for it was focused on what had already gone wrong and whether enough had been set aside for that spilled milk. “Under the incurred loss rules, banks are overstating profits up front and not making prudent provisions against expected losses,” states Iain Richards, of Threadneedle Investments. Thus, CECL was born.

New regulations from the Financial Accounting Standards Board (FASB) are meant to help lenders, and those that invest in them, to better understand the mystery of credit risk. FASB calls this new regulation Current Expected Credit Loss (CECL). The bad news is that CECL left to the accountants is estimated to result in a 50% increase in loss provisions. A 50% rise in loss provisions results from the requirement to estimate expected losses over the life of each lease or loan. Below I’ll show how CECL is ultimately a better way to measure loss provisions, and how equipment finance companies can avoid the 50% increase to loss provisions and hit to return on equity by using estimates of defaults and losses customized to their business models.

Capital Punishment?

FASB, the American Bankers Association and others estimate the immediate impact of CECL could raise loss provisions by 50%. Let’s take the following example:

• $1 million revolving loan is made to a Risk Grade 4 borrower. 
• Risk Grade 4 has a default range of 2.5% – 4.0%.
• The borrower is a chain of dry cleaning stores in business for 20 years.
• Public data shows a range of recovery given default of between 30% and 70%.
• There is collateral (store leases and fixtures), but no recent defaults in dry cleaners have been reported.

Expected Loss = Risk Grade times Migration times Principal times Loss Given Default

These factors allow for interpretation and selection from a range. For example, the Risk Grade 4 indicates a default range of 2.5% to 4.0% or a range of 150 basis points. Principal amount starts at $1 million with more at risk of credit loss at the beginning of the transaction. Recovery rates on collateral can vary by asset type, by stage of the economic cycle and by whether or not the equipment remains essential to the business. The decision to use the high end or low end of these ranges can dramatically affect the calculation of expected loss (EL).

EL = (4.0% X 1.5) X $1MM X 60% = $36,000

The conservative approach will show a $36,000 addition to loan loss provisions.

CECL Nuts and Bolts

Financial assets requiring CECL treatment include trade receivables, loans measured at amortizing cost, net investments in finance leases (including the unguaranteed residual value), off-balance-sheet credit exposures (e.g., loan commitments, standby letters of credit, financial guarantees), all other debt instruments (e.g., debt securities) except available-for-sale debt securities (AFS) and FV-NI securities and reinsurance receivables.

Loan loss provision will happen in initial and subsequent recognition periods. The initial recognition records an allowance to bring the carrying value to the net amount expected to be collected. The ALLL account is a valuation account and is created on day one by a charge to income. Subsequent adjustments reflect expected increases or decreases of expected credit losses that are recognized during the period with the ending allowance equal to the expected loss on assets.

Lenders will provide measurement guidance on annual and interim financial statements. Pooled assets with similar risk characteristic(s), (e.g., risk ratings, asset type, collateral type, size, term, industry, geography, vintage, or patterns) can be valued together. If not using the pooled approach, then assess each asset individually. Factors to consider are past events, including historical experience, current conditions and reasonable and supportable forecasts. Lenders must consider all reasonably available, relevant information; however, they are not required to incur “undue cost and effort” in their efforts. Internal information might be sufficient in determining collectability. Other considerations include prepayments, but do not consider extensions, renewals, and modifications, unless a troubled debt restructuring is reasonably expected.

No models or methods are explicitly prescribed by FASB. Lenders should leverage current systems and methods, but will likely find increased inputs/data and number-crunching. Management should adjust historical loss information for factors relevant to determining expected collectability. In-depth borrower information such as financial asset information, lending policies and procedures, expertise and quality of credit review systems and environmental characteristics (e.g., markets, geographical area, regulatory/legal, etc.) can be considered. Lenders can revert to unadjusted historical loss average experience beyond the forecasted period.

”We’re Gonna Need a Bigger Boat”

In the classic movie Jaws, Police Chief Martin Brody (Roy Scheider) says, “We’re gonna need a bigger boat!” Likewise, lenders are gonna need a bigger data warehouse because CECL models require massive amounts of current and historical data. Most lenders will acknowledge that they either do not possess the amount of data needed for CECL models, or their data collection has been inadequate to date. The risk rating process was meant for regulation and nothing else. Without a good rating scale, migration data has no value. Furthermore, many lenders have no records of prepayments, no records to demonstrate the value of covenants or other structural benefits, and no loss or default databases. Getting this data will be expensive, but the expense can bring big benefits in better decisions, better monitoring, better portfolios, and better reporting.

Capital Appreciation

Let’s go back to our example and outline how a loss provision estimate may be different for an equipment finance company using estimates specific to its business. Dry cleaners are among the safest small businesses. Using the long-term default rate for Dry Cleaners (also conservative) the default risk is 285 bps. Since defaults typically occur 18 months into the life of the loan, exposure at default can be adjusted to 70%. Better intelligence on recoveries shows the collateral equals 50% at default, as opposed to 60%. Migration tables will show that risk changes by a factor of 1.1 based on industry, loan size and beginning probability of default. Therefore, the estimate of expected loss falls from $36,000 to $10,975 as follows:

EL = (2.85% X 1.1) X $0.7MM X 50% = $10,975 

For a $1 billion portfolio, this translates into 200 basis points higher return on equity. The best advice when you’re sick is to get a good doctor. The best advice for dealing with CECL is to get estimates on defaults, collateral loss, exposure at default, and migration that are specific to your type of business.