CECL advice is hitting financial practitioners from all sides. As an industry friend put it, “Now even my dentist has a CECL solution.” With many high-level commentaries on CECL methodologies in publication (including RiskSpan’s ), we introduce this specific framework to help practitioners eliminate ill-fitting methodologies until one remains per segment. We focus on the commercially available methods implemented in the CECL Module of our RS Edge Platform, enabling us to be precise about which methods cover which asset classes, require which data fields, and generate which outputs. Our decision framework covers each asset class under the CECL standard and considers data availability, budgetary constraints, value placed on precision, and audit and regulatory scrutiny. Performance Estimation vs. Allowance Calculations Before evaluating methods, it is clarifying to distinguish performance estimation methods from allowance calculation methods (or simply allowance calculations). Performance estimation methods forecast the credit performance of a financial asset over the remaining life of the instrument, and allowance calculations translate that performance forecast into a single allowance number. There are only two allowance calculations allowable under CECL: the discounted cash flow (DCF) calculation (ASC 326-20-30-4), and the non-DCF calculation (ASC 326-20-30-5). Under the DCF allowance calculation, allowance equals amortized cost minus the present value of expected cash flows. The expected cash flows (the extent to which they differ from contractual cash flows) must first be driven by some performance estimation method. Under the non-DCF allowance calculation, allowance cumulative expected credit losses of amortized cost (roughly equal to future principal losses). These future losses of amortized cost, too, must first be generated by a performance estimation method. Next, we show how to select performance estimation methods, then allowance calculations. Selecting Your Performance Estimation Method Figure 1 below lays out the performance estimation methods available in RiskSpan’s CECL Module. We group methods into “Practical Methods” and “Premier Methods.” In general, Practical Methods calculate average credit performance from a user-selected historical performance data set and extrapolate those historical averages – as adjusted by user-defined management adjustments for macroeconomic expectations and other factors – across the future life of the asset. When using a Practical Method, every instrument in the same user-defined segment will have the same allowance ratio. Premier Methods involve statistical models built on large performance datasets containing instrument-level credit attributes, instrument-level performance outcomes, and contemporaneous macroeconomic data. While vendor-built Premier Methods come pre-built on large industry datasets, they can be tuned to institution-specific performance if the user supplies performance data. Premier Methods take instrument-level attributes and forward-looking macroeconomic scenarios as inputs and generate instrument-level, macro-conditioned results based on statistically valid methods. Management adjustments are possible, but the model results already reflect the input macroeconomic scenario(s). Check marks in Figure 1 indicate the class(es) of financial asset that each performance estimation method covers. Single checkmarks (✔) indicate methods that require the user to provide historical performance data. Double checkmarks (✔✔) indicate methods that, at the user’s option, can be executed using historical performance data from industry sources and therefore do not require the customer to supply historical performance data. All methods require the customer to provide basic positional data as of the reporting date (outstanding balance amounts, the asset class of each instrument, etc.) Figure 1 – Performance Estimation Methods in RiskSpan’s CECL Module [1] Commercial real estate [2] Commercial and industrial loans To help customers choose their performance estimation methods, we walk them through the decision tree shown in Figure 3. These steps to select a performance estimation method should be followed for each portfolio segment, one at a time. As shown, the first step to shorten the menu of methods is to choose between Practical Methods and Premier Methods. Premier Methods available today in the RS Edge Platform include both methods built by RiskSpan (prefixed RS) and methods built by our partner, Global Market Intelligence (S&P). The choice between Premier Methods and Practical Methods is primarily a tradeoff between instrument-level precision and scientific incorporation of macroeconomic scenarios on the Premier side versus lower operational costs on the Practical side. Because Premier Models produce instrument-specific forecasts, they can be leveraged to accelerate and improve credit screening and pricing decisions in addition to solving CECL. The results of Premier Methods reflect macroeconomic outlook using consensus statistical techniques, whereas Practical Methods generate average, segment-level historical performance that management then adjusts via Q-Factors. Such adjustments may not withstand the intense audit and regulatory scrutiny that larger institutions face. Also, implicit in instrument-level precision and scientific macroeconomic conditioning is that Premier Methods are built on large-count, multi-cycle, granular performance datasets. While there are Practical Methods that reference third-party data like Call Reports, Call Report data represents a shorter economic period and lacks granularity by credit attributes. The Practical Methods have two advantages. First, they easier for non-technical stakeholders to understand. Secondly, license fees for Premier Methods are lower than for Practical Methods. Suppose that for a particular asset class, an institution wants a Premium Method. For most asset classes, RiskSpan’s CECL Module selectively features one Premier Method, as shown Figure 1. In cases where the asset class is not covered by a Premier Method in Edge, the next question becomes: does a suitable, affordable vendor model exist? We are familiar with many models in the marketplace, and can advise on the benefits, drawbacks, and pricing of each. Vendor models come with explanatory documentation that institutions can review pre-purchase to determine comfort. Where a viable vendor model exists, we assist institutions by integrating that model as a new Premier Method, accessible within their CECL workflow. Where no viable vendor model exists, institutions must evaluate their internal historical performance data. Does it contain  enough instruments, span enough time ,and include enough fields  to build a valid model? If so, we assist institutions in building custom models and integrating them within their CECL workflows. If not, it’s time a begin or continue a data collection process that will eventually support modeling, and in the meantime, apply a Practical Method. To choose among Practical Methods, we first distinguish between debt securities and other asset classes. Debt securities do not require internal historical data because more robust, relevant data is available from industry sources. We offer one Practical Method for each class of debt security, as shown in Figure 1. For asset classes other than debt securities, the next step is to evaluate internal data. Does it represent (segment-level summary data is fine for Practical Methods) and to drive meaningful results? If not, we suggest applying the Remaining Life Method, a method that has been showcased by regulators and that references Call Report data (which the Edge platform can filter by institution size and location). If adequate internal data exists, eliminate methods that are not asset class-appropriate (see Figure 1) or that require specific data fields the institution lacks. Figure 2 summarizes data requirements for each Practical Method, with a tally of required fields by field type. RiskSpan can provide institutions with detailed data templates for any method upon request. From among the remaining Practical Methods, we recommend institutions apply this hierarchy:

  • Vintage Loss Rate: This method makes the most of recent observations and datasets that are shorter in timespan, whereas the Snapshot Loss Rate requires frozen pools to age substantially before counting toward historical performance averages. The Vintage Loss Rate explicitly considers the age of outstanding loans and leases and requires relatively few data fields.
  • Snapshot Loss Rate: This method has the drawbacks described above, but for well-aged datasets produces stable results and is a very intuitive and familiar method to financial institution stakeholders.
  • Remaining Life: This method ignores the effect of loan seasoning on default rates and requires user assumptions about prepayment rates, but it has been put forward by regulators and is a necessary and defensible option for institutions who lack the data to use the methods above.

Figure 2 – Data Requirements for Practical Methods (Number of Data Fields Required) [3] Denotes fields required to perform method with customer’s historical performance data. If the customer’s data lacks the necessary fields, alternatively this method can be performed using Call Report data. Figure 3 – Methodology Selection Framework Selecting Your Allowance Calculation After selecting a performance estimation method for each portfolio segment, we must select our corresponding allowance calculations. Note that all performance estimation methods in RS Edge generate, among their outputs, undiscounted expected credit losses of amortized cost. Therefore, users can elect the non-DCF allowance calculation for any portfolio segment regardless of the performance estimation method. Figure 5 shows this. A DCF allowance calculation requires the elements shown in Figure 4. Among the Premier (performance estimation) Methods, RS Resi, RS RMBS, and RS Structured Finance require contractual features as inputs and generate among their outputs the other elements of a DCF allowance calculation. Therefore, users can elect the DCF allowance calculation in combination with any of these methods without providing additional inputs or assumptions. For these methods, the choice between the DCF and non-DCF allowance calculation often comes down to anticipated  impact on allowance level. The remaining Premier Methods to discuss are the S&P commercial and industrial loans (C&I) – which covers all corporate entities, financial and non-financial, and applies to both loans and bonds – and the S&P commercial real estate (CRE) method. These methods do not require all the instruments’ contractual features as inputs (an advantage in terms of reducing the input data requirements). They project periodic default and LGD rates, but not voluntary prepayments or liquidation lags. Therefore, users provide additional contractual features as inputs and voluntary prepayment rate and liquidation lag assumptions. The CECL Module’s cash flow engine then integrates the periodic default and LGD rates produced by the S&P C&I and CRE methods, together with user-supplied contractual features and prepayment and liquidation lag assumptions, to produce expected cash flows. The Module discounts these cash flows according to the CECL requirements and differences the present values from amortized cost to calculate allowance. In considering this DCF allowance calculation with the S&P performance estimation methods, users typically weigh the impact on allowance level against the task of supplying the additional data and assumptions. To use a DCF allowance calculation in concert with a Practical (performance estimation) Method requires the user to provide contractual features (up to 20 additional data fields), liquidation lags, as well as monthly voluntary prepayment, default, and LGD rates that reconcile to the cumulative expected credit loss rate from the performance estimation method. This makes the allowance a multi-step process. It is therefore usually simpler and less costly overall to use a Premier Method if the institution wants to enable a DCF allowance . The non-DCF allowance calculation is the natural complement to the Practical Methods. Figure 4 – Elements of a DCF Allowance Calculation I believe the S&P ECL approach is always (even with added prepayment info) a method closely related to, but not a discounted cash flow method, since the allowance for credit losses in S&P approach is calculated directly from the expected credit losses and not as amortized cost minus(-) present value of future cash flows. But this is good since it requires less inputs and easier to relate to macro-economic factors than is a pure DCF. This is consistent with Figure 5. Figure 5 – Allowance Calculations Compatible with Each Performance Estimation Method Once you have selected a performance estimation method and allowance calculation method for each segment, you can begin the next phase of comparing modeled results to expectations and historical performance and tuning model settings accordingly and management inputs accordingly. We are available to discuss CECL methodology further with you; don’t hesitate to get in touch!

Get a Demo