The Consensus Credit Rating maps a raw average Probability of Default (PD) to one of 21 letter-grade buckets. That’s the right format for most use cases — but 21 categories can be too coarse when you need to detect small movements within a grade, or rank entities that sit inside the same CCR bucket. The secondaryDocumentation Index
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CCR100 scale addresses this. It preserves more of the underlying PD signal by mapping the same raw Consensus PD into 101 narrower buckets instead of 21.
CCR100 Publication
Publishing a CCR100 value is a two-step process. Step 1 is covered in full on the Consensus Credit Rating page — this page focuses on Step 2.
Buckets are indexed 1–101, where 101 corresponds to default.
Because this is a table lookup, the published midpoint PD is discrete rather than continuous. In practice it is usually very close to the raw average — but not always exactly equal.
When to use CCR100
Use the headline CCR for credit classification, reporting, and any context where a letter grade is the right output. Use CCR100 when you need to:- Rank entities within the same CCR bucket — two entities both rated
bbbmay sit at meaningfully different positions within that grade - Track small movements — a shift that doesn’t cross a CCR threshold will still show up in the CCR100 bucket
- Feed quantitative models — the underlying average PD and CCR100 midpoint are more suitable continuous inputs than a categorical letter grade