A current-conditions, forecast, and reversion adjustment connects relevant historical loss information to conditions at the reporting date and to reasonable and supportable forecasts. Beyond the supportable forecast period, the estimate returns to historical information through a documented reversion method.
Give each evidence layer one job
Historical data supplies an observed base. Current conditions identify differences between the historical periods and the reporting date. Forecasts address expected future conditions over the supportable horizon. Reversion explains how the estimate moves back to historical information after that horizon.
Assume a pool has a 2.0 percent historical loss rate. Supported current conditions add 0.4 percentage points and a one-year forecast adds 0.3 points. The adjusted rate is 2.7 percent only if the drivers are relevant, measured consistently, and not already present in the historical base. “The economy” is too vague to support either adjustment.
Control overlap and change
Name each driver, source date, affected pool, direction, magnitude, approval, sensitivity, and update trigger. Test whether unemployment, borrower grade, delinquency, or collateral changes appear elsewhere in the model. State the forecast horizon and whether reversion is immediate, straight-line, or another supported method.
Boundary and source
An overlay cannot be a balancing plug. Read ASC 326-20-30-7 through 30-9 for the evidence layers, forecast horizon, and reversion requirement.
Put the concept to work
Understand this concept
- Explain how historical loss information, current conditions, reasonable and supportable forecasts, and reversion perform different evidence jobs.
Analyze this concept
- Evaluate an adjustment for causal relevance, direction, magnitude, source date, double counting, forecast horizon, reversion method, sensitivity, and approval.
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Build on these ideas
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- Current-conditions, forecast, and reversion adjustment — Analyze
Required level here: understand. Required. Evaluation distinguishes the evidence layers before combining them.