Choosing a CECL Methodology | Doable, Defensible, Choices Amid the Clutter
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  Commercial real estate  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.