Investment and risk teams are under growing scrutiny — from internal governance, from boards and from regulators focused on capital adequacy and exposure transparency. Internal credit assessments need to be defensible, and the data behind them needs to come from somewhere independent of the firm itself.

See what banks with skin in the game think about the obligor you’re underwriting: Credit Benchmark provides the consensus credit view of the world’s leading banks’ second-line risk teams on the same names sitting in your portfolio, aggregated and anonymized so no single contributor’s view is exposed. The data is independent, externally sourced and free of issuer-pays conflict — properties traditional credit ratings cannot all claim.

How buy-side credit teams use Credit Benchmark

• Strengthen internal views ahead of IC — reinforce internal ratings with anonymized, aggregate views from a network of regulated financial institutions, strengthening both internal governance and external regulatory dialogue.
• Understand the credit risk quicker — most of the corporates and financial counterparts in our dataset carry no rating from a traditional agency, closing the visibility gap where it matters most.
• Stress test portfolios through periods of volatility — identify which counterparties and banks tend to be consistently conservative or aggressive in their credit posture, sharpening risk differentiation across exposures.
• Early Warning Indicators — weekly refreshed data and built-in early warning indicators flag credit weakening earlier than traditional public ratings, giving teams more time to act.
• Enterprise-wide consistency — feed the same consensus dataset into Investment, Counterparty Risk, Credit Research, Treasury and Operational Due Diligence so the firm speaks one credit language internally.

Outcomes

Stronger validation of internal credit judgments, broader coverage of unrated names, an independent benchmark that satisfies regulators and boards, and a consistent credit lens across investment and risk functions.

Asset managers transact across a wider counterparty universe than ever before: banks and their transactional subsidiaries, global custodians and their sub-custodian networks, prime brokers, execution brokers, exchanges, central counterparties (CCPs) and their clearing members, indemnification providers, other asset managers and underlying funds. Most of these counterparts are not rated by a traditional credit rating agency — and the public data that does exist is rarely timely enough to support pre-trade decisions.

Credit Benchmark provides independent Credit Consensus Ratings across that full universe, drawn from the credit views of the very institutions that face these counterparts on their own balance sheets every day.

How counterparty risk teams use Credit Benchmark

• KYC and Operational Due Diligence (ODD) acceleration — use consensus credit data to screen and prioritize new counterparts efficiently, reducing the time spent chasing information from the counterparts themselves.
• Onboarding and approval — establish counterpart-specific credit policy from day one with an independent reference point, particularly valuable where internal coverage of the name is thin or non-existent.
• Ongoing monitoring and surveillance — set up portfolios and receive automated alerts on credit-quality changes; benchmark internal credit views against the wider market continuously.
• Peer-to-peer transaction support — bring an independent view of view-and-sentiment to peer-to-peer counterpart approvals, supporting your own due diligence with insight into how the market sees the name.
• Business allocation and supply chain decisions — direct business towards counterparts with stronger consensus credit profiles, and monitor concentration risk across the network.
• Treasury and transaction pricing — factor consensus counterparty credit risk into transaction pricing decisions, control counterparty exposures and indemnities, and continuously monitor collateral and counterpart quality across treasury operations.

Counterparty universe covered

Banks and their transactional subsidiaries | Global custodians and sub-custodian networks | External asset managers and underlying funds | Prime brokers and their subsidiaries | Execution brokers | Exchanges, CCPs and clearing members | Indemnification providers | Other non-bank financial counterparts.

Outcomes

Faster, better-supported counterparty onboarding; independent monitoring across the full counterparty universe; earlier detection of counterparty deterioration; defensible counterparty risk decisions with a peer benchmark behind them; and the ability to expand the approved counterparty list — and the revenue that comes with it — by qualifying otherwise-unrated names.

Portfolio managers and credit analysts rely on a mix of inputs: company financials, market-implied spreads, agency ratings, sell-side research, and internal credit views. Credit Benchmark adds an input the buy side has not historically had — the aggregated, anonymized credit view of the world’s leading banks’ second-line risk teams, refreshed weekly and reaching deep into the unrated universe.

Credit Benchmark argues a simple proposition: price discovery in financial markets has long been driven by consensus, and credit risk assessment benefits from the same approach.

Nowhere does this matter more than in private credit. The corporate private credit market continues to grow rapidly, yet most underlying borrowers have no public rating and limited public disclosure. Credit Benchmark coverage routinely reaches the great majority of the unique borrowers held in private credit fund portfolios — providing forward-looking, peer-sourced default risk views on names that would otherwise rely on proxy, sector or vintage assumptions alone.

How portfolio teams use Credit Benchmark

• Pre-trade screening — assess the credit profile of prospective issuers and counterparts before committing capital, including names with no public rating, and understand the sector they operate in and how their peers are performing.
• Forward-looking deterioration signals — net upgrade/downgrade indicators, single-name Opinion Change Indicators and aggregate Credit Risk Indices flag deterioration earlier than agency ratings, supporting timely portfolio repositioning.
• Dispersion and conviction analysis — the spread of contributor views on a single name surfaces conviction or disagreement among the leading banks, an input no single-source rating can provide.
• Sector and geography aggregates — 1,200+ credit indices across 160 countries enable benchmarking exposures, tracking systemic credit risk, and supporting macro and outlook reporting.
• Correlation analysis for diversification — industry correlation matrices reveal genuine credit-driven dependencies across sectors, supporting more robust portfolio construction than spread-based correlation alone.
• Portfolio construction by risk appetite — build and rebalance portfolios against an independent, consistent credit benchmark rather than relying on lagged or fragmented inputs.

Why consensus matters in the investment process

Credit Benchmark data is sourced from the institutions with the deepest credit relationships and the most direct exposures — the banks themselves. It is independent (no issuer-pays conflict), forward-looking (PD-driven, sensitive to credit-quality change), comprehensive (the majority of covered names are unrated by traditional agencies), and dynamic (refreshed weekly). For investment teams accustomed to credit signals that lag the market by months, this changes the timing of what is knowable.

Outcomes

Better-informed pre-trade decisions; earlier detection of credit deterioration in portfolio names; sharper portfolio construction backed by genuine credit correlations; and an independent input that strengthens investment committee discussions, diversification analysis and outlook reporting.

Significant Risk Transfer (SRT) — also referred to as Capital Relief Trades (CRT) — has matured from a European-only product born in the early 2000s into a global market exceeding $20 billion in annual issuance, with US G-SIBs, regional and community banks increasingly tapping the market alongside long-established European issuers. Investor demand has scaled with it: Credit Benchmark works with the majority of the SRT investor community — sovereign wealth funds, hedge funds, multi-strategy asset managers and dedicated SRT specialists alike.

SRT remains a relationship-driven business where information asymmetry between issuer and investor is the norm. Public ratings are rarely available on the underlying portfolios. Market spreads exist for related index products, but reflect sentiment rather than the specific credit composition of the trade in front of you. Credit Benchmark fills that gap with independent, bank-sourced credit consensus data on the underlying counterparts — for both disclosed and undisclosed transactions.

How SRT investors use Credit Benchmark

• Portfolio selection and pre-investment analysis — assess the credit profile of the underlying portfolio against an independent consensus benchmark before committing capital, using weekly-refreshed, anonymized bank-sourced ratings to surface early signs of credit deterioration, rating dispersion and shifting outlooks across candidate deals; identify whether deal pricing compensates for tail risk.
• Disclosed pool benchmarking — for disclosed transactions, run rating notch-difference analysis comparing the issuing bank’s internal ratings against the consensus view of its peers; assess historical PD trends in the relevant sectors.
• Blind pool analysis (via proxy ratings) — when the issuer is not a Credit Benchmark contributor, proxy ratings are derived by filtering the Credit Benchmark database by country, industry and, where available, revenue or NAICS code, generating an average rating, PD and range from a peer cohort of named entities.
• Portfolio diversification analysis — industry correlation matrices across 1,200+ sector aggregates surface genuine credit-driven dependencies between sectors, providing diversification analysis that CDS indices (limited names, often illiquid) and OAS-based correlations (driven by market sentiment) cannot match.
• Transition matrix overlays — global corporate rating transition rates by sector and cohort, tailored to a specific transaction pool, support PD term-structure projection over the life of the trade.
• Ongoing surveillance, hedging and substitutions — track the credit quality of the underlying pool over the lifetime of the transaction; receive alerts on deterioration; inform substitution, hedging and loss-forecasting decisions with current, independent data; surface concentration and correlation risk within a single transaction and across multiple SRT positions.
• Investor reporting and credibility — bring an independent, externally sourced benchmark into investor communications and internal investment committee documentation.

Why consensus data is well suited to SRT

• Rating the unrated — the corporates and financial counterparts in our dataset are largely unrated by traditional credit rating agencies, exactly the population that dominates most SRT portfolios.
• Dynamic — credit consensus refreshed weekly, sensitive to PD-level change long before public ratings move.
• Real-world exposure — sourced from 40+ contributing banks, including roughly half of the world’s G-SIBs, with genuine balance-sheet exposure to the names they rate.
• Independent — free of issuer-pays conflict and free of single-bank bias through aggregation and anonymization.
• Secure — supports both disclosed and undisclosed transactions, with established frameworks for data-sharing agreements where required.
• Expanding footprint — a unique, growing global dataset built specifically for the institutional credit market.

Outcomes

Better-informed SRT investment decisions on both disclosed and undisclosed deals; sharper portfolio diversification analysis; defensible due diligence with an independent benchmark behind it; and the confidence — for both new and seasoned SRT investors — to evaluate a relationship-driven market with data-driven discipline.