Credit Risk Analytics: Market, Portfolio and Benchmarking Insights
The analytical layer of the platform — market indicators, portfolio insights and bank benchmarking, all derived from the Credit Benchmark consensus dataset.
The analytical
layer
Analytics turn the consensus dataset into actionable intelligence. They fall into three groups: Market Analytics (broad market and sector indicators), Portfolio Insights (trends, distributions and migration within a defined universe), and Benchmarking Analytics (how a bank’s own ratings compare with the consensus and its peers). Market and Portfolio analytics are built from the published consensus; Benchmarking Analytics overlay a bank’s own submitted estimates on top of it.
How the analytics
are built
Most analytics are PD aggregates — portfolio-level credit risk indices built by applying Credit Benchmark’s aggregation methodology to a defined universe of entities, using a back-calculation approach anchored to the most recent month. Each aggregate can be expressed three ways: Segment PD Average (the absolute probability of default), Segment PD Rebased (relative change from a chosen base date) or Segment Average Rating (the aggregate PD mapped to the CB rating scale).
Aggregates come in three forms: standard indices on CB’s own universe; custom indices applying CB data to a client’s entity list; and bank indices built from a bank’s own internal PD data using the same methodology. The same calculation process applies in every case.
Market
Analytics
Credit Risk Index
What it is: Credit Benchmark’s flagship monthly index — a snapshot of lender sentiment toward US private corporates and financials. It answers one question: are downgrades outweighing upgrades, or the reverse. A positive CRI signals net downgrades (rising risk); a negative CRI signals net upgrades (improving quality).
How it is derived: Calculated monthly as (downgrades minus upgrades) divided by total entities across a universe of roughly 3,000 privately held US corporates and financials with Credit Consensus Ratings — from actual bank rating actions rather than traded prices. Published at the start of each month.
What is included: The monthly CRI percentage and its history back to 2015; a roughly 3,000-name US private universe (Corporate and Financial entity types); and a signal that reflects views on more than $750B in wholesale loans. The CRI is correlated with annual S&P default rates as a leading indicator and diverges from public high-yield spreads outside crisis periods — capturing the privately financed borrowers that market indices miss.
How it is used: An early-warning read on emerging credit stress among US private corporates and financials, and a complement to spread- and equity-based signals in dashboards and models.
Delivery & documentation: The interactive CRI dashboard, a monthly email briefing, and CSV download of the index and its constituent list.
Industry Aggregates
What it is: Consensus credit risk aggregated into indices by industry, sector and geography — around 1,200 in total.
How it is derived: Standard indices are built on CB’s own universe and segmented according to its six-level industry schema and four-level geographic schema; custom indices apply CB data to a client’s entity list. Each index can be expressed as a PD average, a rebased series or an average rating, and as a net-downgrade (CRI-style) reading.
What is included: Around 1,200 indices spanning sectors, regions and countries, with pre-built segments such as US Corporates, European Financials and Global Technology, and investment-grade / high-yield views.
How it is used: Sector and geographic benchmarking, allocation decisions, and macro and outlook reporting.
Delivery & documentation: Delivery & documentation: Web App (Aggregates), and the API.
Correlation Matrices
What it is: A matrix of credit correlations between proxy indices — industries, sectors and geographies — capturing genuine credit-driven co-movement for diversification, risk budgeting and portfolio optimization.
How it is derived: Computed from monthly consensus PD changes between proxy indices (the Pearson correlation of PD % changes, which removes the trend). Correlations based on Credit Risk Index levels (net downgrades) provide a complementary, sometimes leading, input; Principal Components Analysis can reduce a large index set to a representative few.
What is included: Correlation matrices across the proxy-index schema — for example a 30×30 matrix derived from PD changes — together with PD-volatility and covariance-adjusted portfolio-risk metrics, on both PD-change and CRI-level bases.
How it is used: Diversification analysis, risk budgeting, portfolio structure optimization, and SRT and CLO tranche modeling — quantifying how much genuine diversification a portfolio actually provides.
Delivery & documentation: An Excel portfolio-risk / correlation workbook for clients, plus data files; the underlying proxy indices are available via the API and the Web App (Aggregates).
Credit Transition Matrices
What it is: A matrix showing, for a group of obligors, the proportion that migrate from one rating category to another over a set period, —built from large samples of consensus credit ratings.
How it is derived: Calculated from the consensus history as net rating-change frequencies for a fixed cohort between a start and end period, on the four-, seven- or full twenty-one-category scale. Available as long-run matrices or as credit-cycle-adjusted, time-varying matrices that blend Normal, Downturn and Upturn periods, with separate matrices for Corporates and Financials.
What is included: Around 500 transition-matrix categories across geographies, industries and sectors, with monthly histories of up to 10 years; 7- and 21-category versions; and an optional default column derived from consensus PD midpoints. Bespoke matrices can be built on a client’s own portfolio using consensus history on 120,000+ obligors.
How it is used: Multi-period portfolio risk-drift modeling; default-rate term structures; scenario analysis using existing or simulated matrices; “fallen angel” monitoring; bond and instrument pricing; SRT and CLO tranche modeling; and prioritizing segments for review.
Delivery & documentation: 500+ standard matrices produced upon request plus data files and Excel worked examples for subscribers.
Portfolio
Insights
Portfolio Insights show how credit risk is distributed, trending and migrating within a defined portfolio or universe. They are delivered through the API and the PortfolioLens reporting module found on the My Portfolios page of the Web App — customizable analytics reports generated from a portfolio’s CB data and published to the Web App Inbox for download. PortfolioLens reports include Consensus Trends, Single Name Trends, and for contributing banks, My Rating vs Consensus Trends.
Distribution Analysis
What it is: The share of a portfolio or universe in each rating bucket, and how that distribution shifts over time.
How it is derived: Rating-distribution computation across a scoped universe, with credit-breakdown snapshots by facet (sector, country, industry or any facetable field).
What is included: Rating-bucket shares over time, breakdowns by facet, and the investment-grade / high-yield split.
How it is used: Understanding portfolio composition and concentration, and spotting drift in credit quality.
Delivery & documentation: PortfolioLens, the Web App (My Portfolios) and the API.
Trend Analysis
What it is: How the aggregate credit quality of a portfolio or segment move over time.
How it is derived: An aggregate-trend time series of consensus credit metrics for a scoped universe.
What is included: PD average, rebased and average-rating series over time, and the net direction of movement.
How it is used: Monitoring the credit direction of a portfolio and for macro and outlook reporting.
Delivery & documentation: PortfolioLens (Consensus Trends), the Web App (Aggregates) and the API.
Migration Analysis
What it is: The upgrades and downgrades within a portfolio, and how names move across rating grades over a period.
How it is derived: Entity rating-change over a chosen lookback window, supported by the rating-change fields measured over 1-, 3-, 6-, 9- and 12-month horizons.
What is included: Entity-level upgrades and downgrades, the share of entities moved by one, two or more notches, and the net upgrade/downgrade balance across the portfolio.
How it is used: Surfacing deteriorating and improving names and feeding migration and transition modeling.
Delivery & documentation: PortfolioLens (Single Name Trends), the Web App and the API.
Benchmarking Analytics — Risk Perspective (RPx)
Contributing to Credit Benchmark gives a bank far more than access to a data feed. By submitting their own internal credit risk estimates, contributors gain a privileged view of the consensus they help create — together with a set of capabilities built around it.
That value takes several forms: a triangulated consensus output file that pools the internal assessments of 40+ of the world’s leading banks into a single, cleaned view of credit risk; ongoing data-quality and outlier detection that screens each contributor’s submissions and flags anomalies against the consensus; and a dedicated suite of contributor analytics — Risk Perspective (RPx) — that benchmarks a bank’s own ratings against its peers.
RPx compares a bank’s own internal ratings against the consensus — or a selected peer subgroup of it — at portfolio and entity level, presenting results using the bank’s own grades and PD breakpoints and segmenting by its internal hierarchy. It provides immediate measures of relative conservatism, risk migration and risk-grading behaviours, relative to the full consensus or a chosen peer cohort, and is available only to banks that contribute to the consensus.
Relative Conservatism
What it is: Whether a bank’s grades are systematically more conservative or more aggressive than peers, by industry, region or department.
How it is derived: Notch differences between the bank’s rating and the consensus, measured on the bank’s own scale — negative means more conservative, positive means more optimistic.
What is included: Rating differences by segment in notches, and the direction and degree of conservatism or aggressiveness.
How it is used: Revealing systematic bias and outlier grades, and informing pricing and capital allocation.
Delivery & documentation: Risk Perspective (RPx).
Rating Distribution
What it is: How the bank’s rating distribution across a sample of its loan book compares with peers.
How it is derived: The bank’s distribution of grades set against the consensus distribution, by rating level.
What is included: Where relative conservatism or aggressiveness occurs across the grade spectrum.
How it is used: Seeing where the bank sits versus peers at each rating level.
Delivery & documentation: Risk Perspective (RPx).
Trend Analysis
What it is: How the bank’s average credit risk moves over time across sectors, relative to other banks.
How it is derived: Tracking average credit risk through time and comparing the bank’s trajectory with the consensus, including whether the bank sits above or below the average and whether that position has changed.
What is included: Rating-migration trend lines by sector and outlier positioning over time.
How it is used: Spotting where and when the bank’s ratings diverge from the market — the kind of outlier behavior regulators focus on.
Delivery & documentation: Risk Perspective (RPx).
Lead / Lag Analysis
What it is: Whether the bank’s rating changes precede, coincide with, or follow the consensus.
How it is derived: Lead/Lag Entity Analysis examines single-name timing against the consensus; Lead/Lag Sequencing aggregates this to measure how quickly the bank’s teams update ratings compared with peers.
What is included: Entity-level lead/lag timing and an aggregate measure of rating-update speed versus peers.
How it is used: Identifying proactive versus lagging models and analysts, and flagging process rigidity, insufficient monitoring or understaffed coverage.
Delivery & documentation: Risk Perspective (RPx).
Rating Ordering
What it is: Where the bank’s rank-ordering of credit risk differs from peers.
How it is derived: Rating ordering analyzed across a portfolio by rating and at entity level by sector, with Entity Peer Ranking focusing on a defined group of peer entities.
What is included: Rank-order divergence versus the consensus, and entity peer-ranking inconsistencies.
How it is used: Testing whether the bank’s models rank risk consistently with the market.
Delivery & documentation: Risk Perspective (RPx).
Sector & Geographic Insights
What it is: Divergence hotspots by sector, region or business line.
How it is derived: Segment-level comparison of the bank against the consensus across CB’s sector and geographic schema, or by leveraging the schema of the contributing bank.
What is included: Hotspots flagged by sector, region or business line — for example commercial real estate, energy, EM corporates or leveraged finance.
How it is used: Prioritizing where deeper investigation is needed.
Delivery & documentation: Risk Perspective (RPx).
Tailored to
your bank
RPx is adjusted to a bank’s internal structure so results map directly to its operating model. Analytics are presented using the bank’s own grades and PD breakpoints (Internal Scale Mapping); benchmarking can be against the full set of contributors or a selected peer cohort (Contributor Cohort Selection); and performance can be segmented by internal hierarchy overlays — industry, exposure-driven flags (high/medium/low), scorecard or model, credit officer, line of business, and geography or legal entity — pinpointing exactly where any divergence originates.
So what does this give a bank? Most institutions already run sophisticated risk infrastructure — internal ratings, agency and market signals, and established governance and reporting. What they rarely have is a systematic, repeatable view of how other institutions are positioning and moving risk. RPx supplies that comparative layer, turning the consensus from a reference dataset into evidence a bank can act on.
Because the analytics are pre-computed, built on observation-level information and mapped to the bank’s own structure, they slot into existing governance, portfolio-monitoring and reporting workflows rather than demanding a new one. The payoff reaches across the risk organization: comparative context for the CRO’s strategic and governance decisions, early sight of migration and sector divergence for Portfolio Management, an external reference point for Risk Analytics’ model validation and calibration, and defensible, peer-relative evidence for committee and regulatory reporting. In practice, RPx moves a bank from periodic, manual benchmarking exercises toward an embedded comparative-intelligence capability woven into day-to-day portfolio governance and monitoring.
Put the analytics
to work
Request a data sample or a walkthrough of the analytics most relevant to your team, from market indices to bank benchmarking.
Build on the dataset
Request a coverage check on your own entity universe, or explore the analytics and delivery options built on the consensus dataset.