LIMIT 10;
```
The schema, table, and field definitions match the [Data Dictionary](/data/data-dictionary).
## Need Help?
For provisioning or onboarding questions, contact your Credit Benchmark representative or email [support@creditbenchmark.com](mailto:support@creditbenchmark.com).
# Snowflake Data Delivery
Source: https://docs.creditbenchmark.com/integrations/snowflake
Snowflake access for Credit Benchmark data delivery.
Credit Benchmark data is available as a Snowflake data share, delivered into a dedicated, client-specific database that you query directly from your Snowflake account. No file transfer or ingestion pipeline is required — the data is live in your environment as soon as the share is provisioned.
## Snowflake Marketplace
The Snowflake Marketplace listing is the entry point for requesting or reviewing access to the Credit Benchmark data share from within Snowflake. A Credit Benchmark subscription is still required before the share can be provisioned to your account.
## What You Get
* A dedicated database containing the tables and views included in your subscription.
* Data refreshed in line with Credit Benchmark's standard publication cycle — no client-side jobs required.
* The ability to join Credit Benchmark data against your proprietary datasets in Snowflake without copying it out.
## Prerequisites
* An active Snowflake account in any supported cloud and region.
* A Credit Benchmark subscription that includes Snowflake delivery.
* The Snowflake **account identifier** for the account that should receive the share. Snowflake supports both organisation-account identifiers and legacy account locator formats.
In Snowsight, your account identifier is shown under **Admin → Accounts**, or by running `SELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME();`.
## Getting Access
Let your account manager know you want Snowflake delivery enabled. They will coordinate provisioning with the Credit Benchmark data team.
Send the account identifier of the Snowflake account that should receive the share. If you need the share delivered to multiple accounts (for example, a separate dev or UAT environment), provide each identifier.
Once Credit Benchmark provisions the share, an `ACCOUNTADMIN` in your Snowflake account creates a database from it:
```sql theme={null}
CREATE DATABASE credit_benchmark FROM SHARE .;
GRANT IMPORTED PRIVILEGES ON DATABASE credit_benchmark TO ROLE ;
```
Credit Benchmark will provide the exact provider account and share name during onboarding.
Query one of the views in the new database to confirm the share is live, for example:
```sql theme={null}
SELECT * FROM credit_benchmark.public. LIMIT 10;
```
## Working With the Data
Because the share is read-only and lives in your Snowflake account, you can:
* Query it directly from worksheets, notebooks, or BI tools connected to Snowflake.
* Join Credit Benchmark identifiers against your internal counterparty tables to enrich exposures, watchlists, or limits.
* Build views in your own database that reference the shared objects, without duplicating the underlying data.
The schema, table, and field definitions match the [Data Dictionary](/data/data-dictionary). Field semantics and consensus calculations follow the standard [Methodology](/methodology/intro).
## Security
* The data share is delivered point-to-point between Snowflake accounts; data does not leave Snowflake's managed infrastructure.
* Data is encrypted in transit and at rest by Snowflake.
* Access inside your account is controlled by your Snowflake roles and grants — only roles you explicitly grant `IMPORTED PRIVILEGES` to can read the database.
* Credit Benchmark is ISO 27001 certified.
## Need Help?
For provisioning or onboarding questions, contact your Credit Benchmark representative or email [support@creditbenchmark.com](mailto:support@creditbenchmark.com).
# CCR Distribution Fields
Source: https://docs.creditbenchmark.com/methodology/consensus-calculations/distribution-calculations
Credit Benchmark calculates distribution metrics including standard deviation, skewness, and dispersion on contributed PD estimates for each entity.
Credit Benchmark calculates distribution metrics on contributed 1‑year TTC PD estimates to assess the **spread, shape, and quality** of the consensus for each entity — including how much agreement exists among contributors and whether views are systematically skewed in one direction.
## Calculated Fields
Shows how contributed bank views distribute across the CB rating scale. Contributed PDs are mapped to CB rating buckets using the [Rating Scale](/methodology/rating-scales).
**CCRMin** — the minimum rating in the distribution, derived from the maximum contributed PD:
$$
\text{CCRMin} = f(\max(PD_i))
$$
**CCRMax** — the maximum rating, derived from the minimum contributed PD:
$$
\text{CCRMax} = f(\min(PD_i))
$$
$f(PD)$ is the rating scale mapping function returning a notch index.
**CCRMin Notch** and **CCRMax Notch** are the numeric equivalents of these fields — the integer position on the 21‑point scale rather than the rating label.
Relative dispersion of contributed PDs for an entity — a measure of how much agreement exists among contributing banks.
$$
\text{CCRRSD} = \frac{\sigma_{PD}}{\mu_{PD}}
$$
* $\sigma_{PD}$ = Standard deviation of contributed PD estimates
* $\mu_{PD}$ = Mean of contributed PD estimates
Lower values indicate higher agreement. Published rounded to 1 decimal place.
Asymmetry of the contributed PD distribution — measures whether bank views are tilted relative to the consensus.
$$
\text{CCRSkew} = \frac{1}{n} \sum_{i=1}^{n} \left(\frac{PD_i - \mu_{PD}}{\sigma_{PD}}\right)^3
$$
* $PD_i$ = Individual bank PD estimate
* $\mu_{PD}$ = Mean of contributed PD estimates
* $\sigma_{PD}$ = Standard deviation of contributed PD estimates
* $n$ = Number of contributing banks
Published only when |CCRSkew| ≥ 0.5. Values in (−0.5, 0.5) are not displayed.
Categorical indicator derived from unrounded CCRRSD.
| Level | CCRRSD Range | Meaning |
| ---------- | ------------ | ------------------------------------------------------- |
| **High** | \< 0.6 | Strong consensus; high confidence in the CCR |
| **Medium** | 0.6 – 1.1 | Moderate consensus with some variation |
| **Low** | ≥ 1.1 | Significant disagreement; higher uncertainty in the CCR |
Categorical indicator derived from unrounded CCRSkew.
| Level | CCRSkew Range | Meaning |
| --------------- | ------------- | ----------------------------------------------- |
| **Optimistic** | \< −1 | Banks tend toward lower PDs than the consensus |
| **Balanced** | −1 to 1.6 | Distribution balanced around the consensus |
| **Pessimistic** | ≥ 1.6 | Banks tend toward higher PDs than the consensus |
# Opinion Change Indicator
Source: https://docs.creditbenchmark.com/methodology/consensus-calculations/opinion-change-indicator
The Opinion Change Indicator distinguishes genuine bank opinion changes from consensus rating moves caused by shifts in the contributing bank population.
## Why It Exists
The [Consensus PD](/methodology/consensus-pd) is a simple average — which means it is sensitive to both **what banks think** and **who is contributing**. When the contributor pool changes, the average can shift even if no bank has changed its view.
This means the Consensus PD can move for two very different reasons:
* **Opinion change** — one or more banks changed their PD estimate for this entity
* **Depth change** — the contributing bank population changed; a bank joined, left, or both
Telling them apart is critical to interpreting the data correctly.
## How It Works
The **Opinion Change Indicator (OCI)** measures the net rating change across banks that contributed in both the current and prior period — **holding the population constant** to isolate genuine shifts in credit sentiment.
### Formula
The OCI tracks credit sentiment at the **individual contributing bank level** — for each same-bank contributor, we calculate whether their view moved toward improvement, deterioration, or held steady month-on-month.
Each is assigned a directional signal (**+1**, **−1**, or **0**), and those signals are summed to produce a net read on market direction:
$$
\text{OCI}_t = \sum_{b \,\in\, B_t \,\cap\, B_{t-1}} \text{sign}\bigl( f(PD_{b,t-1}) - f(PD_{b,t}) \bigr)
$$
Where:
* $B_t$ = set of contributing banks at time $t$
* $f(PD)$ = rating scale mapping function returning a notch index
* A positive result indicates net improvement; negative indicates net deterioration
By restricting the sum to $B_t \cap B_{t-1}$ — banks present in both periods — the OCI strips out any movement caused by banks joining or leaving the contributor pool.
If the CCR or Consensus PD moved but the OCI is **Stable**, the move reflects a change in who is contributing — not a change in credit opinion. Weight it accordingly.
## OCI Values
The OCI is published as a directional indicator. On the [single entity page](/delivery-channels/webapp-pages/entity), each contributing bank gets an arrow:
* **Improving** — net same-bank view moved toward lower PDs (green arrow)
* **Stable** — no net change among same-bank contributors (no arrow)
* **Deteriorating** — net same-bank view moved toward higher PDs (red arrow)
## Use in PD Aggregates
The OCI feeds into the [PD Aggregates methodology](/methodology/pd-aggregates/methodology), where population-controlled time series are constructed to track genuine credit sentiment over time — stripping out noise from contributor turnover.
# Rating Comparisons (Notch Differences)
Source: https://docs.creditbenchmark.com/methodology/consensus-calculations/rating-comparisons
Credit Benchmark uses notch differences to compare PD estimates for peer benchmarking, portfolio analysis, and cross-entity risk reporting.
A notch difference is the numerical gap between two PD estimates once both are mapped to a rating scale.
Credit Benchmark uses notch differences in portfolio analytics, peer benchmarking, and time-series tracking — anywhere a relative credit quality comparison is needed.
By default we use the 21-category CB scale; see the [Rating Scale](/methodology/rating-scales). The same concept applies using a bank's internal rating scale — if a mapping is provided, notch differences can be calculated on the bank's own scale.
$$
\text{Notch Difference} = f(PD_i) - f(PD_j)
$$
Where:
* $PD_i$, $PD_j$ — PD estimates from two sources being compared
* $f(PD)$ — rating scale mapping function returning a notch index
* Positive values indicate $PD_i$ is the stronger credit; negative values indicate the reverse
## Applications
$$
f(PD_b) - f(PD_c)
$$
How far a contributing bank's view sits from the market consensus for the same entity.
* **Positive** — bank is more optimistic than consensus (lower PD)
* **Negative** — bank is more conservative than consensus (higher PD)
$$
f(PD_t) - f(PD_{t-n})
$$
How credit quality on an entity has shifted over a chosen period — 1, 3, 6, or 12 months.
* **Positive** — credit quality improved (PD decreased)
* **Negative** — credit quality deteriorated (PD increased)
$$
f(PD_A) - f(PD_B)
$$
Relative credit quality between two entities — used for peer benchmarking and portfolio analysis.
* **Positive** — Entity A has stronger credit quality than Entity B
* **Negative** — Entity A has weaker credit quality than Entity B
# Credit Consensus Rating
Source: https://docs.creditbenchmark.com/methodology/consensus-credit-rating
The Credit Consensus Rating (CCR) is derived from contributed bank PD estimates and mapped to the Credit Benchmark 21-category rating scale.
The **Credit Consensus Rating (CCR)** is Credit Benchmark's core offering — a letter rating for a legal entity derived from a simple average of **1‑year through‑the‑cycle (TTC) Probability of Default (PD)** estimates contributed by our network of banks.
We map this Consensus PD to the Credit Benchmark 21‑category rating scale to produce the CCR shown across our products.
CCR provides an independent view of credit risk based on the aggregated assessments of banks with **skin in the game** — actual lending relationships and real exposure to the entities they rate.
## Credit Benchmark's CCR Process
We receive PD data from over **40 contributing banks** worldwide. Each bank provides their 1-year forward-looking Probability of Default estimates linked to their internal rating systems.
See the [data submission process](/methodology/data-processing/data-submission) for details.
Contributed PDs pass through a comprehensive [data validation process](/methodology/data-processing/data-validation) to ensure quality and consistency before entering the consensus.
The CCR is calculated as a **simple unweighted average** of contributed PDs — a consensus opinion of the default risk of the counterparty.
$$
\text{Consensus PD} = \frac{1}{N} \sum_{i=1}^{N} PD_i
$$
Where:
* $PD_i$ = PD estimate from bank $i$
* $N$ = number of contributing banks (published as [**Contributor Count**](/methodology/contributor-count))
* Publication requires $N \ge 2$
The Consensus PD is mapped to a letter rating using our [Rating Scale](/methodology/rating-scales) lookup table:
$$
\text{CCR} = f(\text{Consensus PD})
$$
Where $f(PD)$ converts PD values into CCR buckets using a standardised scale calibrated from banks' internal rating systems.
## Use Cases
CCR complements internal and CRA ratings by providing an **aggregated market view** from multiple contributing institutions1:
* **Customer onboarding & credit decisioning** — support credit decisions with independent, market-based risk assessments
* **Portfolio monitoring** — track credit quality and identify emerging risks using Consensus PD views
* **Model development & calibration** — validate and calibrate internal models against consensus benchmarks
* **Pricing & valuation** — inform pricing decisions with independent credit perspectives reflecting current market sentiment
* **Regulatory & third-party risk validation** — meet requirements for independent risk validation and third-party governance
* **Entity reference mapping** — standardize entity identification and credit risk mapping across internal systems
1 Banks contributing to Credit Benchmark follow the Basel definition of Default.
# Consensus Probability of Default (PD)
Source: https://docs.creditbenchmark.com/methodology/consensus-pd
The raw Consensus PD is mapped to the CCR100 scale for finer-grained credit analysis, providing 101 buckets versus the standard 21 letter grades.
The [Credit Consensus Rating](/methodology/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 secondary `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 [Credit Consensus Rating](/methodology/consensus-credit-rating) page — this page focuses on Step 2.
Contributed TTC PDs are averaged across all banks:
$$
\text{Consensus PD} = \frac{1}{N} \sum_{i=1}^{N} PD_i
$$
That average is passed through a separate `CCR100` lookup table:
$$
\text{CCR100 PD} = f(\text{Consensus PD})
$$
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 `bbb` may 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
For field-level definitions, see the [Data Dictionary](/data/data-dictionary).
# Contributor count and CCR Source classification
Source: https://docs.creditbenchmark.com/methodology/contributor-count
How Credit Benchmark publishes contributor counts, applies publication thresholds, and distinguishes Consensus from Implied credit ratings using CCRSource.
The **Contributor Count** field indicates how many banks contributed a PD estimate for a given entity. It drives both the publication threshold and the `CCRSource` classification.
## Publication thresholds
Credit Benchmark requires that **two contributor banks** provide PDs on an entity before publishing a Credit Consensus Rating.
To help preserve anonymity around the contributed data, Credit Benchmark will not publish the exact contributor count when there are fewer than 5 contributors. The table below shows the relationship between the **true contributor count** and **what Credit Benchmark publishes.**
| True Contributor Count | CCR Source | Published Contributor Count |
| ---------------------- | ----------------- | --------------------------- |
| 1 | **Not published** | **Not published** |
| 2 | `Implied` | `MIN` |
| 3–4 | `Consensus` | `MIN` |
| 5+ | `Consensus` | `5+` |
Any count below 5 is published as **MIN** rather than the actual number. This prevents users from inferring individual bank contributions when the pool is small.
## Implied ratings
When exactly **2 banks** contribute a PD for an entity, Credit Benchmark introduces a **third obfuscation point** before publishing. This synthetic point is added to prevent reverse engineering of individual bank views from the published average — a concern that is more acute when the contributor pool is very small.
The obfuscation point is designed **not to move the average**, so the published rating still accurately reflects the views of the contributing banks. It adds protection without distorting the signal.
The `CCRSource` field is set to `Implied` in this case to signal that the published rating is derived from a protected pool rather than a full Consensus.
## When to use CCRSource
Filter on `CCRSource = "Consensus"` when you want to work only with ratings backed by 3 or more independent bank views. Implied ratings are valid credit signals but carry more uncertainty and a degree of obfuscation by design.
# Bank Data Onboarding
Source: https://docs.creditbenchmark.com/methodology/data-processing/bank-onboarding
Credit Benchmark reviews sample PD files, validates contributor models, and onboards new contributing banks through a structured process before go-live.
Before onboarding a new contributor bank, Credit Benchmark runs a **structured analysis** to ensure data is fit for inclusion in the consensus. These discussions can run in parallel with other client onboarding workstreams. We review contributor models and data, examine **sample PD files**, and work collaboratively with client teams and data owners to investigate potential outliers or differences versus history and peers. Confirmed issues are resolved before **go‑live**.
## Pre-Onboarding Review
As part of this analysis, we:
* **Verify PD scope and definitions** — 1‑year TTC; senior unsecured; wholesale/commercial book; default definition alignment
* **Evaluate PD scale** — confirm whether the bank's PD scale aligns with other contributors or whether practices are inconsistent with existing contributors
* **Screen sample submissions for anomalies** — dispersion/RSD, large movements; confirm outliers with the bank
* **Validate identifiers and mapping approach** — support accurate entity resolution (LEI/ticker where available)
In select cases, Credit Benchmark has opted to **not include** contributed data in the consensus. CB will still receive these data points for reporting and concordance purposes, but will not use them when publishing CCRs.
## Onboarding Questionnaire
Contributors complete a structured methodology **questionnaire** confirming **eligibility and comparability** of contributed PDs, documenting governance and data practices, and establishing operational readiness for ongoing submissions.
### What we confirm in each area
* 1‑year TTC obligor PDs
* Senior unsecured
* Wholesale/commercial book
* Out‑of‑scope items (facility‑level, retail, asset‑backed)
*Example: Do your 1‑year PDs represent obligor‑level TTC views for senior unsecured exposure in the wholesale/commercial book?*
* Alignment with the Basel definition of default (or jurisdictional equivalent)
*Example: Which default definition do you apply in production?*
* Model validation
* Approvals and overrides
* Watchlist and early warning
* Re‑rating triggers
* Review cadence
*Example: How are rating overrides raised, approved, and tracked?*
* Minimum monthly snapshot
* Cut‑off dates
* Backfill and roll‑forward rules
*Example: What is your monthly submission timeline and how do you handle backfills/corrections?*
* Identifiers (LEI, ticker, national IDs)
* Internal rating
* PD and as‑of date
* Model or scorecard
* Firmographics
* Explicit non‑collection: no facility data, exposure, MNPI, or financial inputs
*Example: Which identifiers will you provide per obligor and what coverage do you expect?*
* Guarantees and credit enhancements
* Consolidated vs. legal entity
* Branches and subsidiaries
* Funds and SPVs
* Sovereigns and public sector
*Example: How do you treat guarantees and group structures?*
* Notification of model changes and recalibrations
* Methodology drift
* Known data limitations
*Example: How will you notify CB of model changes or recalibrations?*
* Secure transport method
* Operational and escalation contacts
*Example: What secure transfer method will you use and who are the contacts?*
# Data Submission
Source: https://docs.creditbenchmark.com/methodology/data-processing/data-submission
Over 40 contributing banks submit entity-level Probability of Default estimates to Credit Benchmark on a monthly or weekly data cadence.
Over **40 contributing banks** submit PD data to Credit Benchmark on a regular cadence. Submissions include obligor-level **1-year through-the-cycle (TTC) PDs** linked to internal ratings for **senior unsecured** exposure in the wholesale/commercial book. We work with each contributor during onboarding to map their files into the core data elements needed for processing and validation.
## Submission Process
Contributors deliver updates at least once per month:
Each delivery contains a current snapshot of risk assessments and the core context needed for consensus calculation:
* Latest **1-year TTC PD** estimates linked to internal ratings
* Coverage across Corporates, Governments, Funds, and Financials
* Scope limited to **senior unsecured** exposure in the commercial/wholesale book
* Agreed data fields and entity identifiers required for processing
Banks deliver through Credit Benchmark's secure production channels — browser-based **CB Secure** or automated **SFTP**. Where needed, CB can connect to a client's existing secure portal or file transfer process agreed during onboarding.
Submissions undergo automated and manual checks as part of our [Data Validation](/methodology/data-processing/data-validation) process to ensure data quality and consistency.
## Delivery Options
| Delivery Method | Typical Use Case |
| --------------------------------- | ---------------------------------------------------------- |
| **CB Secure** | Manual upload via web browser |
| **SFTP** | Automated or scheduled file transfer |
| **Client-managed portal or SFTP** | CB connects to a client's existing secure delivery process |
## Data Collection Scope
Standardized risk measures and entity identifiers to support consensus calculations.
| Data Category | Details |
| ---------------------- | ------------------------------------------------------------ |
| **Entity Identifiers** | Legal name, country, identifiers (e.g., LEI where available) |
| **Risk Measures** | 1-year TTC PD, internal rating, and as-of date |
| **Model Information** | Model or scorecard identifier used to produce the PD |
| **Firmographics** | Entity type and sector/industry classification |
We do not collect sensitive operational details or underlying credit analysis inputs.
| Data Category | Details |
| ------------------------ | ----------------------------------------------------- |
| **Facility Information** | Terms, collateral schedules, covenants |
| **Exposure Data** | Loan size, limits, utilizations |
| **Underlying Inputs** | Financial statement inputs or raw borrower financials |
## PD Acceptance Criteria
| Requirement | Description |
| -------------- | ------------------------------------------------------------------------------------------ |
| **Type** | Obligor-level 1-year through-the-cycle (TTC) PDs |
| **Exposure** | Senior unsecured exposure in wholesale/commercial book |
| **Governance** | Produced under bank's model governance and linked to internal ratings |
| **Standard** | Aligned to regulated TTC conventions (e.g., Basel capital PD or jurisdictional equivalent) |
| Type | Reason |
| ------------------------ | --------------------------------------------------------- |
| **System-generated PDs** | Without governed model output or analyst approval |
| **Facility-level PDs** | Collateralized or asset-backed PDs |
| **Retail PDs** | Personal consumer PDs and products |
| **Point-in-time PDs** | Scenario/stress-only PDs that don't reflect TTC standards |
## Entity Concordance
The consensus process begins with entity resolution — ensuring submissions from multiple banks are matched to the same underlying legal entity before any calculations are performed. This is supported by a dedicated team of **25+ specialists** combining contributor-supplied identifiers with commercial reference data, public registries, and country-specific databases.
The matching process:
* Prioritises direct identifiers such as LEI and ticker where available
* Handles naming variations such as "Apple Inc" versus "Apple INC US"
* Cross-checks multiple reference sources before assigning a match
* Assigns one `CBId` per entity for downstream consensus processing
**Example: resolving Apple Inc. across four banks**
| Bank | Submitted Name | Identifier Provided |
| ------ | ----------------- | ------------------- |
| Bank A | "Apple Inc" | LEI |
| Bank B | "Apple INC US" | LEI |
| Bank C | "Apple Inc. (US)" | Ticker: AAPL |
| Bank D | "APPLE INC" | None |
When identifiers are present we align deterministically. When absent, we review names and supporting reference data before resolving to a single `CBId`.
Once entities are resolved, CB assigns its own industry classifications using our [Industry Classification](/methodology/data-processing/industry-classification) methodology.
## Security and Compliance
Credit Benchmark is certified to the following standards:
* **ISO 27001:2022** — Information Security Management System
* **SOC 2 Type 2** — Security, Availability, and Confidentiality controls
* **SOC 2 Type 1** — Security, Availability, and Confidentiality controls
* **ISO 27017** — Cloud Security Controls
For up-to-date information on security controls and regulatory certifications, visit our [Security and Compliance page](https://compliance.creditbenchmark.com/).
# Data Validation
Source: https://docs.creditbenchmark.com/methodology/data-processing/data-validation
Credit Benchmark validates contributed PD data through layered controls including outlier detection, cross-bank checks, and trend analysis before consensus.
Credit Benchmark validates contributed data to support reliable consensus outputs. Before PD validation begins, contributions are matched to the correct legal entity — see [Data Submission](/methodology/data-processing/data-submission) for how entity resolution works.
## Observation-Level PD Validation
Each monthly submission passes through layered controls at the observation level before it can contribute to the published consensus. The purpose is to identify data inconsistencies or outliers where a PD value is materially different from:
* **Other contributions from different banks** — a single extreme outlier can skew the consensus average. Cross-contributor comparison identifies values that are anomalous relative to the broader market view for that entity.
* **Previous contributions from the same bank** — banks can and do make large downgrades legitimately. The threshold here is set high to catch only extreme shifts — the kind more likely to be a submission or processing error than a genuine credit reassessment. Moves that cross the threshold are reviewed and cleared before publication.
An observation that fails a control is temporarily excluded from consensus eligibility until it is cleared or otherwise handled per policy.
## Quarantine Rules
Credit Benchmark applies several classes of checks to validate PD data and identify outliers. The published methodology summarises the control framework rather than listing every internal threshold or operating rule.
* **Relative Standard Deviation (RSD)** — standard deviation divided by mean across contributed PDs for an entity; higher values indicate wider dispersion
* **Logarithmic PD distance** — the log-scale distance of an observation from the entity median; large negative values indicate unusually low PDs, large positive values indicate unusually high PDs
* All checks are performed on **1-year TTC PDs**
| Stage | What we review | Typical outcome |
| --------------------------- | -------------------------------------------------------------------------------- | ---------------------------------------------------------- |
| **Duplicates** | More than one current observation submitted by the same bank for the same entity | One record retained or held for manual review |
| **Consensus spread** | Observation sits materially away from the current contributor set | Unusual values quarantined pending review |
| **Month-on-month movement** | PD has moved unusually far versus the same bank's prior view | Large moves reviewed before publication |
| **New entrant screening** | First-time or returning contribution inconsistent with the existing consensus | Additional scrutiny applied before entering the pool |
| **Default classification** | How 10,000 bps observations should be treated under publication policy | Technical and potential default cases handled consistently |
## Persistent Outliers
Any PD that remains an outlier for multiple cycles is treated as a **persistent outlier** and stays quarantined until either:
* **Client confirmation** — the bank confirms the view is intentional
* **PD change** — the PD changes for that entity and the observation is re-evaluated
This ensures persistent data-quality issues and genuine changes in credit view are both handled consistently.
# Industry Classification
Source: https://docs.creditbenchmark.com/methodology/data-processing/industry-classification
Credit Benchmark maps NAICS, SIC, NACE, and other classification standards to a unified CB industry schema for consistent analytics across all entities.
Contributors and external sources may provide NAICS, SIC, NACE, ANZSIC, or proprietary labels. We use these as inputs, but the output is always a **single CB industry assignment** — enabling consistent analytics across all entities and banks.
## Sources of Classifications
Banks use a range of internal and external schemas. Common standards include:
* **NAICS** — North American Industry Classification System
* **SIC** — Standard Industrial Classification
* **NACE** — European statistical classification of economic activities
* **ANZSIC** — Australian and New Zealand Standard Industrial Classification
Others use **proprietary schemas** tailored to their portfolios. The same entity may therefore appear under different industry labels across contributors.
## Mapping to the CB Industry Schema
We derive a single consensus industry classification for each entity using a transparent, repeatable process:
Banks submit their internal industry labels and codes alongside entities.
We maintain canonical mapping tables for NAICS, SIC, NACE, and ANZSIC, and convert contributed labels to a common set.
Bank inputs are compared against external registries including Companies House (UK), OpenCorporates, UK Register House, the Italian Business Registry, and other national registries. This validates and enriches the contributed classifications.
Inputs are reconciled to a single Credit Benchmark industry label used consistently across banks and products. Each monthly submission is parsed, mapped, and tracked for changes across cycles.
## Example: Assigning a CB Industry
Three banks submit industry metadata for the same entity — one with a NAICS code, one with a SIC code, one with an ANZSIC code. Using our maintained mappings, these are reconciled to a **single CB industry**. The original source codes are retained for reference.
| Bank Contributed Code | Code Definition | CB Sub-Sector |
| --------------------- | ----------------------------- | ------------- |
| NAICS 484000 | Truck Transportation | Trucking |
| SIC 4213 | Trucking and Courier Services | Trucking |
| ANZSIC 4610 | Road Freight Transport | Trucking |
For field-level definitions and the full CB schema, see [Industry Schema Definitions](/data/industry-schema-definitions).
# Credit Methodology
Source: https://docs.creditbenchmark.com/methodology/intro
Credit Benchmark collects, processes, and publishes credit consensus ratings from PD estimates submitted by the world's leading financial institutions.
Credit Benchmark collects **1-year through-the-cycle (TTC) Probability of Default (PD)** estimates from over 40 of the world's leading financial institutions and aggregates them into consensus credit analytics — published weekly at the entity and portfolio level.
Contributions are governed, validated, and processed through a defined pipeline before publication. Individual bank views remain completely confidential.
## What's in this section
* **[Credit Consensus Rating](/methodology/consensus-credit-rating)** — how PD estimates become a letter rating
* **[Rating Scales](/methodology/rating-scales)** — the CB 21-category scale and agency alignment
* **[Data Processing](/methodology/data-processing/data-submission)** — submission, validation, and entity resolution
* **[Consensus Calculations](/methodology/consensus-calculations/distribution-calculations)** — distribution, skew, opinion change, and rating comparisons
* **[PD Aggregates](/methodology/pd-aggregates/overview)** — portfolio and sector-level analytics
# Aggregate Calculation Methodology
Source: https://docs.creditbenchmark.com/methodology/pd-aggregates/methodology
Portfolio PD aggregates are calculated using a back-calculation approach anchored to the most recent month and validated by the Opinion Change Indicator.
The Aggregate PD is a portfolio-level metric reflecting average credit risk across a defined entity set.
The goal is to produce an index on a **moving pool of entities** that captures **genuine bank opinion changes** — filtering out moves caused purely by entities entering or leaving the universe.
It is built using a **back-calculation approach** — anchoring to the most recent month as a baseline, then working backwards through time using only month-on-month PD changes validated by the [Opinion Change Indicator (OCI)](/methodology/consensus-calculations/opinion-change-indicator).
This ensures the time series stays consistent with the current portfolio view and filters out noise from contributor population shifts.
The back-calculation approach means the most recent month always reflects the full current entity universe. Historical values are derived from that baseline — so the series never distorts when entities join or leave.
## Mathematical Process
Transform the PD for each entity $i$ at time $t$ into log space:
$$
\operatorname{LogPD}_{i,t}=\log\!\bigl(\operatorname{PD}_{i,t}\bigr)
$$
Compute the month-on-month change in log PD for each entity:
$$
\Delta\operatorname{LogPD}_{i,t} = \operatorname{LogPD}_{i,t}-\operatorname{LogPD}_{i,t-1}
$$
Retain only changes where the OCI confirms a genuine shift in bank opinion. If the sign of the OCI does not match the sign of the log PD change, set the difference to zero:
$$
\operatorname{Adj}\Delta\operatorname{LogPD}_{i,t}=
\begin{cases}
\Delta\operatorname{LogPD}_{i,t},
& \text{if }\mathrm{sign}(\mathrm{OCI}_{i,t})
= \mathrm{sign}\!\bigl(\Delta\operatorname{LogPD}_{i,t}\bigr)\\[4pt]
0, & \text{otherwise}
\end{cases}
$$
Average the adjusted log differences across all $n$ entities for month $t$:
$$
\overline{\Delta\operatorname{LogPD}}_{t} = \frac{1}{n}\sum_{i=1}^{n}\operatorname{Adj}\Delta\operatorname{LogPD}_{i,t}
$$
Establish the baseline by averaging raw log PDs across all entities at $t_{\text{latest}}$:
$$
\operatorname{AggLogPD}_{t_{\text{latest}}} = \frac{1}{n}\sum_{i=1}^{n}\operatorname{LogPD}_{i,t_{\text{latest}}}
$$
This is the starting point for all back-calculation.
Derive each prior month by subtracting the average log difference:
$$
\operatorname{AggLogPD}_{t-1} = \operatorname{AggLogPD}_{t}-\overline{\Delta\operatorname{LogPD}}_{t}
$$
Repeat iteratively for all months prior to $t_{\text{latest}}$.
Exponentiate to return to the probability scale:
$$
\operatorname{AggPD}_{t}=\exp\!\bigl(\operatorname{AggLogPD}_{t}\bigr)
$$
## Output Views
The Aggregate PD can be expressed three ways:
The absolute probability of default for the segment — the direct output of the back-calculation:
$$
\operatorname{AggPD}_{t} = \exp\!\bigl(\operatorname{AggLogPD}_{t}\bigr)
$$
Relative change from a chosen base date $t_0$, showing directional risk movement:
$$
\text{RelChange}_t(\%) = \frac{\operatorname{AggPD}_{t}-\operatorname{AggPD}_{t_0}}{\operatorname{AggPD}_{t_0}} \times 100
$$
The aggregate PD mapped to the CB rating scale:
$$
\text{Rating} = f(\operatorname{AggPD}_{t})
$$
Where $f$ is the CB PD-to-rating mapping function; see the [Rating Scale](/methodology/rating-scales).
# PD Aggregates Overview
Source: https://docs.creditbenchmark.com/methodology/pd-aggregates/overview
Credit Benchmark builds portfolio-level credit risk indices from contributed PD data, covering CB Aggregates, custom aggregates, and client aggregates.
PD Aggregates are portfolio-level credit risk indices built by applying Credit Benchmark's aggregation methodology to a defined universe of entities. The same calculation process applies regardless of which data source or entity set is used — see [Aggregate Calculation Methodology](/methodology/pd-aggregates/methodology).
## Three Types of Aggregates
Pre-built indices using Credit Benchmark's **PD data and entity universe**, segmented according to the [CB industry schema](/data/industry-schema-definitions). These are ready-to-use benchmarks for common market segments — US Corporates, European Financials, Global Technology, and more.
Use these for benchmarking a portfolio against a **published CB market view**.
Indices built from Credit Benchmark's PD data applied to **your specific entity list**. You define the universe and segmentation; we supply the Consensus PD data and run the aggregation.
Use these when you want **CB's independent credit view** on your own portfolio or peer group.
Indices built from **your bank's own internal PD data**, aggregated using the CB methodology. Applies the same rigorous calculation process to your portfolio that powers CB's standard indices.
Use these for consistent **time-series analysis of your internal book** using a standardised aggregation approach.
## Output Views
Each aggregate can be expressed three ways depending on the analysis:
* **Segment PD Average** — absolute probability of default for the segment at each point in time
* **Segment PD Rebased** — relative change from a chosen base date, showing directional risk movement
* **Segment Average Rating** — aggregate PD mapped to the CB rating scale
![Portfolio aggregate rebased view]()
# Alignment with Rating Agencies
Source: https://docs.creditbenchmark.com/methodology/rating-agency-alignment
How Credit Benchmark's Credit Consensus Ratings compare with S&P and Fitch, including rating distributions and upgrade and downgrade patterns.
Credit Benchmark's Credit Consensus Ratings (CCR) are derived from contributing banks' internal credit opinions and rating mappings, rather than from agency ratings themselves, but in practice they often align closely with major credit rating agencies such as S\&P and Fitch.
## CCR vs. S\&P and Fitch: Rating Distribution
The chart below shows **a comparison of Credit Benchmark CCR against S\&P and Fitch ratings across 5,000+ entities**. Each point represents a rating pair: one CCR value and one agency rating for the same entity at the same point in time. Bubble size reflects the number of entities in each combination.
![Bubble chart comparing Credit Benchmark CCR to available S&P and Fitch ratings across 5,000+ entities]()
Most observations cluster close to the 45‑degree line, and CCR and agency ratings **agree for the majority of entities**. Where gaps exist, they typically reflect timing differences, methodology differences, or coverage variations.
## Why alignment occurs
* **The Credit Benchmark scale is aligned to rating agency categories by construction**: the CB [rating scale](/methodology/rating-scales) is calibrated from banks' own internal grade-to-agency mappings.
Contributing banks map their internal grades to agency-style categories (AAA, BBB, etc.), and the CB scale is built from the average of those mappings.
Agency comparability is built into Credit Benchmark's PD to CCR scale.
* **Banks and agencies respond to the same risk drivers**: bank credit models and agency analysts assess the same underlying fundamentals.
Their views naturally converge toward similar levels for most entities.
* **Consensus aggregation flattens idiosyncratic variation**: CCR pools 40+ banks' views.
Differences in individual bank frameworks tend to average out, leaving the signal closer to the market consensus.
CCR is typically directionally and level-wise comparable to S\&P and Fitch, while remaining an independent, higher-frequency signal that can identify shifts in perceived risk ahead of published agency actions.
# Credit Benchmark Rating Scale
Source: https://docs.creditbenchmark.com/methodology/rating-scales
Credit Benchmark maps Probability of Default values to Credit Consensus Ratings through its standardized 21-category scale, from aaa through d.
This page explains how Credit Benchmark maps Probability of Default (PD) values to Credit Consensus Ratings (CCR) through its standardized 21-category scale. If you are new to Credit Benchmark, start with the [CCR overview](/methodology/consensus-credit-rating).
## Credit Benchmark Rating Scale
Credit Benchmark's rating scale provides a standardized way to translate Probabilities of Default (PDs) into a credit rating. The scale ranges from **aaa** (highest credit quality) to **d** (default).
Our scale is a consensus construct derived from the PD scales of **40+ banks**. Each bank maintains its own master scale, including PD cutoffs for each internal grade. We aggregate those PD cutoffs across contributors and interpolate a single set of lower and upper through-the-cycle (TTC) PD boundaries for each category in the Credit Benchmark 21-category scale.
These boundaries are:
* **Aggregated from multiple independent sources** - A true consensus, not a vendor opinion
* **Expressed as annual TTC PD ranges** - To reduce procyclicality
* **Reviewed periodically** - To maintain stability and comparability across sectors, regions, and size buckets
### Rating Categories and PD Mappings
All PD bounds are expressed in basis points (bps). For example, 30 bps = 0.30%.
| Notch | Rating | PD Lower (bps) | PD Upper (bps) | Category |
| ----- | -------- | -------------- | -------------- | -------- |
| 1 | **aaa** | 0 | 1.25 | IG |
| 2 | **aa+** | 1.25 | 2.25 | IG |
| 3 | **aa** | 2.25 | 3.25 | IG |
| 4 | **aa-** | 3.25 | 4.75 | IG |
| 5 | **a+** | 4.75 | 6.25 | IG |
| 6 | **a** | 6.25 | 8.5 | IG |
| 7 | **a-** | 8.5 | 14 | IG |
| 8 | **bbb+** | 14 | 20 | IG |
| 9 | **bbb** | 20 | 30 | IG |
| 10 | **bbb-** | 30 | 48 | IG |
| 11 | **bb+** | 48 | 76 | HY |
| 12 | **bb** | 76 | 112 | HY |
| 13 | **bb-** | 112 | 195 | HY |
| 14 | **b+** | 195 | 365 | HY |
| 15 | **b** | 365 | 650 | HY |
| 16 | **b-** | 650 | 1000 | HY |
| 17 | **ccc+** | 1000 | 1700 | HY |
| 18 | **ccc** | 1700 | 2500 | HY |
| 19 | **ccc-** | 2500 | 3700 | HY |
| 20 | **cc** | 3700 | 6800 | HY |
| 21 | **c** | 6800 | 10000 | HY |
| — | **d** | — | — | Default |
Credit Benchmark also maintains a secondary `CCR100` scale for finer PD-based granularity in some published fields. The headline CCR continues to use the 21-category scale. See [Consensus PD](/methodology/consensus-pd) for how `CCR100` fields are published.
## How We Create the Consensus Scale
Credit Benchmark's PD scale represents the collective wisdom of **40+ leading global banks**. Each bank has its own internal rating system with unique naming conventions and PD boundaries. Our challenge is to harmonize these disparate systems into a single, standardized consensus scale.
### The Challenge: Multiple Rating Systems
Each contributor bank has its own internal rating system with unique naming conventions:
| Bank Type | Example Rating System |
| -------------------- | ------------------------------------- |
| **European Bank** | 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 |
| **US Regional Bank** | A1, A2, A3, B1, B2, B3, C1, C2, C3, D |
| **Asian Bank** | AAA, AA, A, BBB, BB, B, CCC, CC, C, D |
### The Solution: Standardization Process
We create a consensus definition of what PD maps to each credit grade by using each bank's internally assigned **Credit Rating Agency Equivalent** (e.g., S\&P, Moody's, Fitch) as our common language. This allows us to compare like-for-like credit quality across different internal scales.
Each bank provides us with its internal rating definitions and how they map to standard credit rating agency equivalents. For example:
* **European Bank's 6** -> Equivalent to S\&P BBB
* **US Regional Bank's B1** -> Equivalent to S\&P BBB
* **Asian Bank's BBB** -> Equivalent to S\&P BBB
This common reference point allows us to compare PD estimates across different internal rating systems.
For each S\&P equivalent rating (e.g., **BBB**), we collect the PD values from all banks and analyze the lower, midpoint, and upper boundaries contributed across the network. For example:
* **European Bank** (Grade 6) — 17–32 bps, midpoint 23 bps
* **US Regional Bank** (B1) — 19–34 bps, midpoint 25 bps
* **Asian Bank** (BBB) — 18–31 bps, midpoint 24 bps
*Example data for illustration. Actual contributor values are confidential.*
Aggregating across all contributors for this grade produces the Consensus PD range used in Step 3.
We analyze the PD distributions across all banks for each rating category and calculate the consensus boundaries:
* **BBB consensus range**: 20-30 basis points
* **BBB midpoint**: 25 basis points
* This becomes our **bbb** rating range in the Credit Benchmark scale
The process is repeated for each rating category to create the full Credit Benchmark 21-category rating scale.
## Annual Review and Alignment
Our PD mapping table is evaluated periodically to ensure boundaries remain aligned with the consensus across all contributor banks. This review process maintains the scale's accuracy and relevance as bank risk models and market conditions evolve.
For a deeper discussion of how our consensus ratings compare with S\&P and Fitch, see [Alignment with Rating Agencies](./rating-agency-alignment).
# Bloomberg
Source: https://docs.creditbenchmark.com/partnerships/bbg
Access Credit Benchmark data via the Bloomberg Terminal and Bloomberg Enterprise Data License.
Credit Benchmark data is distributed on Bloomberg through two channels: the **Bloomberg Terminal** for interactive use, and the **Bloomberg Enterprise Data License (EDL)** service for systematic redistribution into client systems.
## Available Data Sets
| Data set | Coverage |
| -------------------------------- | ----------------------------------------------------------------------- |
| Credit Consensus Ratings | \~70,000 mostly unrated companies across developed and emerging markets |
| Bond and Loan Rating Assessments | \~160,000 bonds and loans, \$28+ trillion outstanding |
Consensus ratings are aggregated from anonymized internal risk views of 40+ contributing banks. Field semantics follow the standard [Methodology](/methodology/intro) and [Data Dictionary](/data/data-dictionary).
## Prerequisites
* An active Bloomberg Terminal license, or a Bloomberg Enterprise Data License agreement.
* A Credit Benchmark subscription that includes Bloomberg distribution.
## Getting Access
Let your account manager know whether you need Terminal access, EDL distribution, or both. They will coordinate entitlement with Bloomberg.
Bloomberg enables Credit Benchmark fields on the relevant Terminal users or EDL feeds. Existing Terminal users do not need a separate login.
Sign in to the Bloomberg Terminal as normal. Once entitlement is active, CB fields are available across the functions listed below.
## Where to Find CB Data on the Terminal
CB data is surfaced throughout the Bloomberg Terminal rather than in a dedicated function. Common entry points:
* **CRPR** — corporate ratings page, where CB Consensus Ratings appear alongside other rating sources.
* **SRCH** — security search, filterable on CB fields.
* **Worksheets** — pull CB fields into custom worksheet columns.
* **Launchpad** — embed CB fields in monitors and dashboards.
* **Excel API** — request CB fields directly from Bloomberg's Excel add-in for spreadsheet workflows.
![Credit Consensus Ratings on the Bloomberg Terminal CRPR screen]()
## Enterprise Data License
For systematic delivery into client systems (data warehouses, risk engines, downstream applications), CB data is available through Bloomberg's EDL service rather than the Terminal. Contact your Credit Benchmark representative to scope an EDL feed.
## Need Help?
For Terminal entitlement issues, contact your Bloomberg representative. For questions about the underlying Credit Benchmark data, contact your Credit Benchmark representative or email [support@creditbenchmark.com](mailto:support@creditbenchmark.com).
# Preqin
Source: https://docs.creditbenchmark.com/partnerships/preqin
Access Credit Benchmark Credit Consensus Ratings inside Preqin Pro.
Credit Consensus Ratings (CCRs) are available inside Preqin Pro as an add-on to a Preqin subscription. CB data is surfaced in Preqin Pro searches and embedded on Investor, Fund Manager, Fund, and Company profiles — no separate UI to learn.
## Preqin Help Center
## Prerequisites
* An active Preqin Pro subscription.
* The Credit Consensus Ratings add-on enabled on your Preqin account.
## Getting Access
Existing Preqin subscribers should contact their Preqin account manager and request the Credit Consensus Ratings add-on. New users should contact Preqin sales.
Notify your Credit Benchmark representative that you are enabling CCRs through Preqin so they can confirm coverage and any cross-platform questions.
Once the add-on is provisioned, sign in to Preqin Pro with your existing credentials.
## Where to Find CB Data in Preqin Pro
* **Search** — filter by Credit Consensus Rating, probability of default, agreement indicator, industry, and country across **Investors**, **Fund Managers**, **Funds**, and **Companies & Deals**.
* **Profiles** — CCR fields appear directly on individual Investor, Fund Manager, Fund, and Company profiles.
* **Charts** — historical trend charts are interactive and exportable for use outside Preqin.
## What's Included
* Independent risk measures on rated and unrated entities globally.
* Over 1 million monthly credit observations sourced from 40+ contributing banks.
* AAA–D consensus rating scale, with PD and agreement indicators alongside.
Field semantics match the standard [Methodology](/methodology/intro) and [Data Dictionary](/data/data-dictionary).
## Need Help?
For questions about Preqin Pro access, contact your Preqin account manager. For questions about the underlying Credit Benchmark data, contact your Credit Benchmark representative or email [support@creditbenchmark.com](mailto:support@creditbenchmark.com).
# Release Notes
Source: https://docs.creditbenchmark.com/product-updates/index
Release notes for Credit Benchmark: API launches, new endpoints, data and schema changes, documentation updates, and other platform announcements.
Track product launches, data updates, documentation changes, and Web App improvements across Credit Benchmark.
The Entity Resolution API resolves external company names to Credit Benchmark reference entities - standardize identifiers before portfolios load, before analytics run, before anything downstream depends on the data being clean.
* One endpoint: POST /matching/text/match\_external
* Returns ranked candidates with CBId, entity name, country of risk, and confidence score
* Optional hints - country, industry, LEI - sharpen results for noisy names
[Read the full update](/product-updates/matching-api-launch)
The updated Excel Add-In is now available through Microsoft Marketplace, making it easier to install and start using Credit Benchmark data in Excel.
* Install from the Microsoft Marketplace listing
* Bring consensus credit risk data into Excel for portfolio monitoring and issuer analysis
* Compare internal views against benchmark data in recurring reporting workflows
[Read the full update](/product-updates/excel-add-in-marketplace)
Pull portfolio trends, rating moves, sector breakdowns, and raw entity data into your own dashboards and reports — without exporting from the web app.
* Track trends, rating changes, sector breakdowns, rating distribution, or raw entity data
* Same portfolio, refreshed on a schedule
* Full schemas and examples in the API reference
[Read the full update](/product-updates/analytics-api-revamp)
Everything you need to work with Credit Benchmark - methodology, data reference, API docs, and product guides - now lives in one place at docs.creditbenchmark.com.
* Methodology, Data Dictionary, API Reference, and User Guide at launch
* Interactive API docs with schemas and worked examples
* A structured home for ongoing release notes and updates
[Read the full update](/product-updates/documentation-site-launch)
Credit Consensus Ratings are now available inside Preqin Pro through a Credit Benchmark partnership - available to Preqin subscribers as an add-on, with no need to leave the platform.
* Searchable across Investors, Fund Managers, Funds, and Companies & Deals
* Embedded on individual investor, fund manager, fund, and company profiles
* Historical trend charts, exportable for use outside Preqin
[Read the full update](/product-updates/preqin-integration)
CB data now includes subdivision-level geography - state, province, territory, or region - so you can slice portfolios within countries, not just across them.
* Three new fields: CBSubdivision, CBSubdivisionISO, CBSubdivisionRegion
* Available in Screener, portfolio views, exports, and entity pages
* Covers \~80% of the CCR universe
[Read the full update](/product-updates/cb-subdivision-field)
The Credit Benchmark Web App now supports French and Spanish alongside English. Switch languages at any time - the underlying workflows stay exactly the same.
* English, French, and Spanish available from the language selector
* Localized navigation and interface text across all core workflows
* Switch without leaving the application
[Read the full update](/product-updates/multilanguage-support)
You can now upload entity lists by name in My Portfolios. CB handles the matching - no need to pre-map everything to CBId before you can get started.
* Paste a list or upload CSV/XLSX directly
* CB maps submitted names to reference entities and CBId automatically
* Reduces manual cleanup for portfolios with inconsistent naming
[Read the full update](/product-updates/entity-name-matching)
The Web App has been rebranded. New visual identity, cleaner interface, same workflows - nothing about how you use the product has changed.
* New logo and updated color scheme
* Improved contrast and streamlined navigation
* Core workflows untouched
[Read the full update](/product-updates/webapp-rebrand)
PortfolioLens adds downloadable analytics reports for your portfolios - consensus trends, entity movement, and internal-versus-consensus comparisons - delivered straight to your Inbox.
* Portfolio-level consensus movement, entity rating trends, consensus vs. internal
* Reports generated from CB data and published to Inbox for download
* Built for recurring reviews and credit monitoring
[Read the full update](/product-updates/portfoliolens-analytics)
Credit Benchmark data now refreshes every Friday morning (ET) across all delivery channels - Web App, Excel, output files, file feeds, and third-party integrations.
* Moved from twice monthly to weekly publishing
* More current data for monitoring, reporting, and alerting workflows
* Month-end stamps preserved for trend analysis
[Read the full update](/product-updates/weekly-data-updates)
Product Updates is the running record of everything that changes at Credit Benchmark - new features, data updates, interface improvements, and announcements.
[Read the full update](/product-updates/welcome)
# Aggregate Trend
Source: https://docs.creditbenchmark.com/api-reference/analytics/aggregate-trend
/openapi/consensus-data.yaml post /beta/data/aggregatetrend
Time series of aggregate credit metrics for a scoped entity universe. Use to track how consensus credit quality moves over time.
# Credit Breakdown
Source: https://docs.creditbenchmark.com/api-reference/analytics/credit-breakdown
/openapi/consensus-data.yaml post /beta/data/creditbreakdown
Credit quality snapshot broken down by a facet column — sector, country, industry, or any facetable field.
# Entity Rating Change
Source: https://docs.creditbenchmark.com/api-reference/analytics/entity-rating-change
/openapi/consensus-data.yaml post /beta/data/entityratingchange
Entity-level rating changes over a lookback window. Use to identify upgrades and downgrades across a portfolio.
# Rating Distribution
Source: https://docs.creditbenchmark.com/api-reference/analytics/rating-distribution
/openapi/consensus-data.yaml post /beta/data/ratingdistribution
Share of entities in each rating bucket over time for a scoped universe.
# Create JWT Token
Source: https://docs.creditbenchmark.com/api-reference/create-jwt-token
/openapi/consensus-data.yaml post /api/security/token
Create a JWT bearer token.
# Get Data
Source: https://docs.creditbenchmark.com/api-reference/data/get-data
/openapi/consensus-data.yaml post /beta/data/getdata
Raw entity-level data for a scoped universe across a time range. The primary endpoint for extracting point-in-time or time series data.
# Entity Resolution
Source: https://docs.creditbenchmark.com/api-reference/entity-name-resolution
/openapi/consensus-data.yaml post /beta/text/match_external
Resolves entity names to Credit Benchmark identifiers. Returns ranked candidates with confidence scores.
# Available Data Columns
Source: https://docs.creditbenchmark.com/api-reference/metadata/available-data-columns
/openapi/consensus-data.yaml get /beta/metadata/columns
Returns a flat catalog of public input columns with metadata such as type, scopeable, facetable, and entitlement. This endpoint describes request-side input columns only; it does not describe route response fields or result_filter fields.
# Available Dates
Source: https://docs.creditbenchmark.com/api-reference/metadata/available-dates
/openapi/consensus-data.yaml get /beta/metadata/available-dates
Returns latest and previous available effective dates for analytics requests.
# Geography Schema
Source: https://docs.creditbenchmark.com/api-reference/metadata/geography-schema
/openapi/consensus-data.yaml get /beta/metadata/geography-schema
Returns the CB geography hierarchy as region group, region, and country of risk levels.
# Industry Schema
Source: https://docs.creditbenchmark.com/api-reference/metadata/industry-schema
/openapi/consensus-data.yaml get /beta/metadata/industry-schema
Returns the CB industry hierarchy as L1 through L6 rows for scope filters, facets, and hierarchy-aware analytics.
# Rating Scales
Source: https://docs.creditbenchmark.com/api-reference/metadata/rating-scales
/openapi/consensus-data.yaml get /beta/metadata/rating-scales
Returns CB rating scale metadata, including CB21 and CB7 ordering and band mappings.
# Aggregate Trend
Source: https://docs.creditbenchmark.com/api-reference/analytics/aggregate-trend
/openapi/consensus-data.yaml post /beta/data/aggregatetrend
Time series of aggregate credit metrics for a scoped entity universe. Use to track how consensus credit quality moves over time.
# Credit Breakdown
Source: https://docs.creditbenchmark.com/api-reference/analytics/credit-breakdown
/openapi/consensus-data.yaml post /beta/data/creditbreakdown
Credit quality snapshot broken down by a facet column — sector, country, industry, or any facetable field.
# Entity Rating Change
Source: https://docs.creditbenchmark.com/api-reference/analytics/entity-rating-change
/openapi/consensus-data.yaml post /beta/data/entityratingchange
Entity-level rating changes over a lookback window. Use to identify upgrades and downgrades across a portfolio.
# Rating Distribution
Source: https://docs.creditbenchmark.com/api-reference/analytics/rating-distribution
/openapi/consensus-data.yaml post /beta/data/ratingdistribution
Share of entities in each rating bucket over time for a scoped universe.
# Create JWT Token
Source: https://docs.creditbenchmark.com/api-reference/create-jwt-token
/openapi/consensus-data.yaml post /api/security/token
Create a JWT bearer token.
# Get Data
Source: https://docs.creditbenchmark.com/api-reference/data/get-data
/openapi/consensus-data.yaml post /beta/data/getdata
Raw entity-level data for a scoped universe across a time range. The primary endpoint for extracting point-in-time or time series data.
# Entity Resolution
Source: https://docs.creditbenchmark.com/api-reference/entity-name-resolution
/openapi/consensus-data.yaml post /beta/text/match_external
Resolves entity names to Credit Benchmark identifiers. Returns ranked candidates with confidence scores.
# Available Data Columns
Source: https://docs.creditbenchmark.com/api-reference/metadata/available-data-columns
/openapi/consensus-data.yaml get /beta/metadata/columns
Returns a flat catalog of public input columns with metadata such as type, scopeable, facetable, and entitlement. This endpoint describes request-side input columns only; it does not describe route response fields or result_filter fields.
# Available Dates
Source: https://docs.creditbenchmark.com/api-reference/metadata/available-dates
/openapi/consensus-data.yaml get /beta/metadata/available-dates
Returns latest and previous available effective dates for analytics requests.
# Geography Schema
Source: https://docs.creditbenchmark.com/api-reference/metadata/geography-schema
/openapi/consensus-data.yaml get /beta/metadata/geography-schema
Returns the CB geography hierarchy as region group, region, and country of risk levels.
# Industry Schema
Source: https://docs.creditbenchmark.com/api-reference/metadata/industry-schema
/openapi/consensus-data.yaml get /beta/metadata/industry-schema
Returns the CB industry hierarchy as L1 through L6 rows for scope filters, facets, and hierarchy-aware analytics.
# Rating Scales
Source: https://docs.creditbenchmark.com/api-reference/metadata/rating-scales
/openapi/consensus-data.yaml get /beta/metadata/rating-scales
Returns CB rating scale metadata, including CB21 and CB7 ordering and band mappings.