Excerpt from the Discounted Cash Flow whitepaper in Abrigo’ Practical CECL Transition series

Discounted Cash Flow (DCF) models, while not widely adopted as a means to account for the allowance for loan and lease losses (ALLL) under ASC 450-20 (current GAAP), have been accepted as best practice for adherence to other analogous accounting standard objectives. For example, fair value measurement (ASC 820) and purchased credit-impaired (ASC 310-30) both inherently require an understanding/estimate of lifetime credit loss. Forward-focused cash flow models are commonly deployed to accommodate both fair value and purchased credit-impaired requirements resulting in an approach that has a precedent of successful audits and examinations.

All implementation and data considerations being equal, DCF is the method of choice for most portfolio concentrations. This is mainly due to the methodological alignment with the accounting standard’s requirements to estimate lifetime losses while effectively giving proper consideration and support for prepayment behavior, timing and forecasted conditions.

Generating and interpreting a contractual schedule of cash flows for financial assets is not theoretically difficult, nor is it conceptually challenging to generate and interpret a schedule of expected cash flows. However, the mechanics of generating and analyzing such schedules can prove cumbersome without the assistance of purpose-built software. When software is available, a DCF election may be the most practical approach to CECL. Alternatively, when software is not available, the long-term benefits provided by the capability of executing portfolio-wide cash flow projections may serve, by itself, as the justification for software acquisition or internal development.

Benefits in electing a DCF approach include but are not limited to the following:

  • Contractual Life – each loan is projected based on contractual terms, avoiding an “average-life” requirement
  • Reasonable and Supportable Forecast – quantified timing and volatility
  • Data – mitigated internal database limitations
  • Decreased reliance on loan-level historical databases
  • Applicable and publicly available peer/industry data
  • Flexibility – inputs and assumptions allow for specific portfolio considerations and data limitations
  • Defensible – highly auditable framework as outcomes are numerical and factual
  • Environmental impact is quantified and directionally consistent
  • Reduced reliance on top-down qualitative adjustments

To learn more about DCF outputs; inputs, assumptions, and sensitivities; forecast application; specific guidance; and conceptual soundness under the context of ASU 2016-13 (Topic 326), download the full DCF whitepaper from Abrigo’ Practical CECL™ Transition series.

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