Overview

State Street Corporation is a leading global financial services provider with US$4.7 trillion in assets under management and a US$25.6 billion market cap. The company delivers investment servicing, investment management and financing solutions to institutional clients worldwide. Its Front-Office Risk team plays a critical role in managing credit, counterparty and market risk, while identifying growth opportunities and aligning them with Enterprise Risk Management (ERM) guidelines.

Challenge

State Street’s Front-Office Risk team wanted visibility into how their internal ratings compared with the market. Key challenges included:

• Limited transparency into market consensus and peer group ratings.
• Difficulty in identifying and justifying credit rating changes to ERM, especially for emerging opportunities (e.g., firms with crypto exposure).
• A desire to uncover growth opportunities that required expanding risk guidelines.

In the absence of external validation, it was difficult to initiate risk-based conversations with internal stakeholders and challenge internal credit ratings for counterparties.

Approach

Credit Benchmark’s consensus data enables State Street to benchmark internal ratings against industry peers, providing:

• Risk insight for surveillance of counterparty limits, collateral, and exposures based on ratings comparison.
• Alerts for changes in CRA ratings and consensus shifts.
• Benchmarking tools to evaluate how quickly State Street adjusts ratings versus the broader market.

In the absence of external validation, it was difficult to initiate risk-based conversations with internal stakeholders and challenge internal credit ratings for counterparties.

Outcome

Credit Benchmark’s (CB) data has delivered measurable benefits:

• Efficiency: Streamlined evaluation of existing and prospective counterparties, enabling faster, more targeted risk decisions.
• Leadership buy-in: Strengthens case for expanding risk appetite using peer-aligned data.
• Continuous monitoring: Enables the Front-Office Risk team to monitor risk on an on-going basis, and utilize CB data to identify areas of focus.
• Strategic value: Insights help prioritize focus areas and support for internal rating changes, unlocking business opportunities.
• Revenue expansion: Where State Street has a more conservative counterparty rating – CB data can be used to conduct enhanced due diligence for upgrade assessment.

Overview

Standard Bank Group is Africa’s largest bank by assets and a leading pan-African financial services provider. Headquartered in Johannesburg and listed on the JSE (SBK), it serves over 19 million clients across 20+ African countries. 

Operating at scale in data-constrained markets, Standard Bank provides end-to-end financial services spanning corporate and investment banking, retail and business banking, wealth management, insurance, foreign exchange, and digital banking. With approximately R3.4 trillion (~$192bn) in assets, the group faces structurally complex credit risk across its African footprint. 

Within the Risk function, the Wholesale Credit Model Development team builds regulator-credible IRB models to accurately differentiate risk across African counterparties. A Credit Benchmark client since 2017, Standard Bank uses Credit Benchmark as an independent external reference point to validate internal models, strengthen governance, and support objective, efficient credit decision-making. 

Challenge

Standard Bank Group is Africa’s largest bank by assets and a leading pan-African financial services provider. Headquartered in Johannesburg and listed on the JSE (SBK), it serves over 19 million clients across 20+ African countries. 

Operating at scale in data-constrained markets, Standard Bank provides end-to-end financial services spanning corporate and investment banking, retail and business banking, wealth management, insurance, foreign exchange, and digital banking. With approximately R3.4 trillion (~$192bn) in assets, the group faces structurally complex credit risk across its African footprint. 

Within the Risk function, the Wholesale Credit Model Development team builds regulator-credible IRB models to accurately differentiate risk across African counterparties. A Credit Benchmark client since 2017, Standard Bank uses Credit Benchmark as an independent external reference point to validate internal models, strengthen governance, and support objective, efficient credit decision-making. 

Approach

Standard Bank licenses Credit Benchmark consensus credit data for its Risk function and uses it as an independent benchmark across model development, validation, and governance. 

Key applications include: 

• Testing model ranking power: Credit Benchmark enables objective assessment of how well internal models rank risk, replacing subjective analyst comparisons where agency coverage is weak. 
• Improving decision efficiency: By providing a third, independent data point, Credit Benchmark reduces opinion-led debate and shortens internal credit discussions. 
• South Africa–focused benchmarking: Bespoke peer sets reflecting local market participants help avoid skew introduced by global benchmarks that may not reflect regional realities. 
• Portfolio-level insights: Beyond single-name views, aggregated regional and sector data supports portfolio risk assessment and reporting. 
• LGD governance support: Regional and sector-level LGD averages provide a defensible starting point for LGD overrides in data-constrained markets. 
• Operational integration: Credit Benchmark ratings are being embedded directly into internal credit reports used in approval committees, reducing friction for analysts. 

   

Outcome

Credit Benchmark is used as part of Standard Bank’s credit modelling and governance framework. It supports more objective model validation, clearer internal discussions, and earlier identification of potential model issues—reducing regulatory and capital risk. 

The data strengthens confidence in internal models, improves peer benchmarking for African counterparties, and provides credible evidence to support model overrides and expert judgement. Credit Benchmark is also well understood by internal audit, external audit, and regulators, and is routinely included in model documentation and reporting. 

As usage matures, Standard Bank is expanding into portfolio analytics and exploring deeper, more automated IRB-aligned benchmarking—particularly around LGD and override analysis. 

Overview

eSecLending is an independent securities financing agent that designs and manages customized securities lending programs for institutional beneficial owners. Its model emphasizes risk-adjusted performance, transparency, and disciplined counterparty oversight, particularly in workflows where indemnification is supported by internal capital and external insurance. 

The Challenge: Establishing Credit Confidence Across Securities Financing Workflows

Securities lending and repo activity depend on credit confidence across multi-party transactions. When commercially important counterparties, such as state pension plans or other non-bank institutions, have no external rating or limited public credit disclosure, participants may lack the independent credit evidence needed to proceed with confidence. 

For eSecLending, limited ratings coverage can create friction across underwriting, borrower approval, indemnity-policy eligibility, third-party due diligence, and participation in peer-to-peer structures. The need continues after approval, as market participants require timely, comparable insight to monitor creditworthiness, identify changes in credit profile, and evaluate counterparties against relevant peers. 

The Solution: Incorporating Credit Benchmark Across Approval, Diligence, and Monitoring Workflows

Credit Benchmark gives eSecLending a weekly refreshed consensus view of real-world credit risk, aggregating banks’ internal credit perspectives rather than relying on a single agency opinion. With approximately 90% of its universe unrated by credit rating agencies, Credit Benchmark extends visibility to entities where traditional coverage may be limited. 
 
For eSecLending, this creates a reliable benchmark for both internal credit review and external validation, helping insurers, clearing house stakeholders, and other transaction participants gain comfort with counterparty credit quality where public ratings may be limited or unavailable. 

• Insurance-supported eligibility— eSecLending’s insurers accepted a framework where a Credit Benchmark consensus score satisfied minimum eligibility requirements under its policy when traditional ratings were unavailable. 
• Enhancing underwriting— Credit Benchmark complements the credit process for new and existing counterparties with a credible benchmark that helps ground internal risk discussions and support more informed counterparty review. 

The Impact: More Supportable Approvals, Stronger Diligence, and Broader Monitoring Context

By incorporating Credit Benchmark into its credit-risk toolkit, eSecLending can support a more structured credit framework leading to: 

• Expanded approval pathways for selected unrated or under-covered counterparties
• Greater third-party confidence for insurers, central counterparty stakeholders, and other transaction participants
• Timelier monitoring context across portfolios, sectors, and peer groups

Overview

Rand Merchant Bank (RMB), a division of FirstRand Bank Limited, is a leading African corporate and investment bank within one of the continent’s largest financial services groups. RMB provides advisory, funding, trading, corporate banking, and investing solutions, supported by FirstRand’s extensive African and global network. Known for its inventive, collaborative culture and deep specialist expertise, RMB funds and advises transactions across more than 35 African countries. 

Headquartered in Johannesburg, RMB employs approximately 4,000 people, while the broader FirstRand Group employs over 50,000 and reported R1.72 trillion (USD $15.1B) in revenue. 

RMB has been a Credit Benchmark customer since 2018, with a license held by the central credit risk function, supporting use cases across structured credit trading, buy-side lending, and portfolio credit risk management. 

Challenge

RMB operates in a highly competitive, low-growth environment marked by frequent regulatory change, pressure on capital efficiency, and intense scrutiny of credit decisions. In this context, credit teams are frequently required to justify internal models, ratings, and assumptions both to regulators and to internal stakeholders such as deal teams and senior management. 

Like many banks, RMB also faces a persistent dynamic where credit teams are challenged on whether decisions are too conservative, particularly when deals are lost in competitive situations. With a low default portfolio and limited internal loss data, benchmarking internal views against the broader market is critical—but must be done carefully and responsibly. 

At the same time, RMB’s use of third-party data is deliberately controlled and selective, driven by internal legal guidance. This meant that any external data source needed to deliver clear, defensible value in the specific moments it was used—particularly in model validation, portfolio reporting, and capital discussions. 

Approach

RMB uses Credit Benchmark’s consensus credit risk data as an independent reference point to validate internal views and support evidence-based decision-making. 

Key use cases include: 

• Annual validation and portfolio reporting: Credit Benchmark data is incorporated into RMB’s annual validation process and internal portfolio reporting packs. The team summarises where market consensus exists, visualises trends, and highlights the top 20 exposures with the largest notching differences versus internal views. 
• Capital models and management reporting: Credit Benchmark provides an external benchmark that supports discussions around model calibration, capital optimisation, and management reporting, particularly in an environment of ongoing regulatory pressure. 
• Hands-on support and analysis: Credit Benchmark actively supports RMB with bespoke analysis and training when required, helping the team respond efficiently to internal questions and regulatory demands. 
• Expanding use cases: RMB and Credit Benchmark are actively exploring broader applications of the data, including early warning indicator (EWI) use cases. 

Outcome

Credit Benchmark enables RMB to operate with greater confidence, accuracy, and efficiency in credit risk management. 

Key outcomes include:  

• Unbiased validation of internal views: Consensus data provides an objective benchmark that helps balance internal discussions and substantiate credit decisions. 
• More efficient internal conversations: By grounding debates in independent data, RMB reduces repetitive challenges and streamlines discussions between credit, business, and management teams. 
• Stronger governance and model confidence: Credit Benchmark supports model validation and capital discussions with credible external evidence, particularly valuable in a constrained regulatory environment. 
• Clear value today, with upside ahead: Even with limited internal distribution, RMB continues to see strong value from Credit Benchmark—and recognises significant opportunity to extend usage further, particularly for EWI and proactive risk monitoring. 

Overview

Canadian Derivatives Clearing Corporation (CDCC), a wholly-owned subsidiary of the Montréal Exchange (MX), acts as the central clearing counterparty (CCP) for derivative products traded on MX in Canada and for a growing range of customized financial instruments. CDCC occupies a unique position in the Canadian financial markets; its role is to ensure the integrity and stability of the market it supports. In addition:

• CDCC is the only integrated CCP in North America that clears and settles futures, options, and options on futures.
• CDCC has a track record of more than 35 years as the CCP for exchange-traded derivative products in Canada.
• CDCC has more than 30 clearing members, including major Canadian financial institutions and brokers.

Like other CCPs worldwide, CDCC promotes fair market access to its different products , and effectively balances access to participants while applying robust monitoring practices, a process critical to anticipating and reacting to potential changes in participant creditworthiness.

How CDCC Uses Credit Benchmark

In order to harmonize the credit assessment of the different types of participants, CDCC employs data from Credit Benchmark, which serves to augment its risk assessment of clearing members and counterparties.

This data, available via the Application Programming Interface (API) and the Credit Benchmark Web Application, offers significant insights, especially concerning private and unrated entities.

Information is used by CDCC to refine its decision-making processes, manage counterparty exposures, and strengthen its risk oversight capabilities.

Approach

Credit Benchmark’s consensus data enables State Street to benchmark internal ratings against industry peers, providing:

• Risk insight for surveillance of counterparty limits, collateral, and exposures based on ratings comparison.
• Alerts for changes in CRA ratings and consensus shifts.
• Benchmarking tools to evaluate how quickly State Street adjusts ratings versus the broader market.

In the absence of external validation, it was difficult to initiate risk-based conversations with internal stakeholders and challenge internal credit ratings for counterparties.

Overview

A leading global alternative asset management firm focused on niche investment strategies has grown its global investor base by combining complementary trading strategies and deep investment expertise across asset classes. Managing a complex multi-strategy portfolio requires agile, high-quality credit risk management, particularly under growing regulatory pressure surrounding capital adequacy and exposure transparency.

Challenge

The firm’s risk and investment teams needed:

• Independent and credible credit benchmarks to validate internal credit exposures and satisfy both internal governance and external oversight.
• Deeper contextual insight into credit behavior across their banking counterparties, identifying institutions with consistently conservative or aggressive risk postures.
• Forward-looking data to evaluate potential expansion into structured credit, especially collateralized loan obligations (CLOs), by aligning consensus credit data to underlying loan-level risk.

Approach

The firm integrated Credit Benchmark’s consensus credit risk data into its core risk oversight and counterparty management processes, including for risk transfer (SRT) structures:

• Independent Validation: Used anonymized, aggregate credit views from a network of regulated financial institutions to reinforce internal ratings and withstand regulatory scrutiny.
• Behavioral Analysis: Applied Credit Benchmark’s scorecard tools to understand how counterparties behave relative to consensus, improving risk differentiation across bank exposures.
• Strategic Exploration: Began evaluating new structured credit strategies, mapping consensus data to CLO portfolios for enhanced transparency and risk targeting.

Outcome

By embedding Credit Benchmark’s data, the firm achieved:

• Stronger Risk Validation: Added an independent lens for validating internal credit judgments and reinforcing their overall risk management approach.
• Improved Counterparty Insight: Gained a more nuanced understanding of the risk behavior and rating tendencies of key banking relationships. 
• Greater Strategic Confidence: Enabled more informed consideration of CLO strategies and other credit-linked investments with enhanced visibility into loan-level risk.

The firm continues to leverage peer-sourced credit insights to evolve its credit risk frameworks and strengthen its position in an increasingly complex credit environment.

Overview

The Credit & Portfolio Risk Management team at this leading global specialty insurer acts as a strategic and control function, ensuring that underwriting profitability and enterprise stability are balanced against evolving credit and systemic risks.

Challenge

The client required:

• Deeper and differentiated credit risk insights across its portfolio
• Reliable alternatives to rating agency data, which were occasionally inaccurate and incurred additional fees for custom requests.

Approach

To strengthen their decision-making and streamline risk assessment, the client:

• Screened new opportunities through the Underwriter, then transferred them to the Country, Credit & Portfolio Risk Management team.
• Integrated Credit Benchmark data via API, enabling seamless access to consensus credit risk views.
• Filled public rating gaps or created comparables using Credit Benchmark consensus data, supporting risk calibration.
• Adopted Credit Benchmark’s intuitive credit algorithm, empowering faster, more informed decisions.

Looking ahead, the client is planning to:

• Implement a new decision track, where Credit Benchmark ratings will be a checkbox item—expected to reduce ticket turnaround time and quantify time savings.
• Extend usage to other product lines through a centralized obligor database.

Outcome

By leveraging Credit Benchmark, the client achieved:

• Faster Decision-Making: “Credit Benchmark enables us to say yes more quickly and potentially get bigger underwriting deals approved.”
• Increased Confidence: Tracking rating trajectories improves internal conviction and supports regulatory scrutiny.
• Enhanced Insight: “With Credit Benchmark data, we gain the summarized perspective of 10+ experienced analysts in 1 consensus rating.”
• Operational Efficiency: Time saved by Credit Benchmark’s summarized obligor profiles and highlighting peer group comparisons where no rating exists.
• Flexible Support: Anomalies are addressed through responsive, tailored service.
• High Usability: “Credit Benchmark is the data provider we most like working with – the ease of use is attractive.”
• Strategic Advantage: Provides the “luxury of banking insight” often unavailable to insurers.

Client Overview & Challenge 

Our client is a large global bank with over $1T in total assets, operating globally across banking, securities, asset management, and other services.  

Our client identified limitations in traditional credit rating coverage – particularly for funds and specialized finance entities. These gaps presented challenges in developing robust internal models and achieving comprehensive visibility across the full spectrum of counterparties. The firm sought an independent, data-driven source to enhance credit assessments and meet evolving regulatory expectations.  

The Solution: Integrating Credit Benchmark Credit Consensus Ratings (CCRs)  

Our client engaged Credit Benchmark to provide a complementary layer of insight through its credit consensus data. Two teams are actively leveraging the data:  

• Wholesale Credit Risk Modeling: This team integrates CCRs into internal credit risk models alongside S&P and Moody’s ratings. Credit Benchmark data is instrumental in model recalibration, validation, and enhancing risk sensitivity – ultimately improving model defensibility in regulatory interactions.  

• Credit Risk Management Team: The Credit Risk Management team accesses Credit Benchmark’s web-based platform to conduct granular entity-level reviews, enriching the team’s understanding of counterparties beyond the traditional rating spectrum.  

The Impact: Greater Coverage, Confidence, and Credibility  

Our client has found Credit Benchmark data particularly transformative in areas such as project finance and investment funds, where conventional ratings are often sparse or absent. The depth, breadth, and objectivity of consensus-based insights have delivered measurable value in:  

• Expanding risk coverage in under-rated segments
• Enhancing model robustness and transparency
• Supporting internal and regulatory reporting requirements
• Empowering decision-makers with timely and credible credit reviews. 

By integrating Credit Benchmark’s data, our client strengthens its credit risk frameworks with a broader, data-rich perspective, driving more informed, agile, and defensible decisions.  

Overview 

This U.S. commercial bank, with more than $250 billion in assets, uses Credit Benchmark’s consensus credit risk data to complement its internal credit risk framework. By incorporating an independent benchmark into model validation, portfolio monitoring and governance activities, the bank has strengthened confidence in its internal credit assessments while enhancing oversight across its commercial portfolio. 

The Challenge

Managing a large commercial lending portfolio requires continuous validation of internal credit assessments and confidence that risk models remain appropriately calibrated. The bank sought an independent source of credit intelligence to complement its existing framework and support: 

• Independent validation of internal ratings and models 

• Early identification of obligors whose risk profile diverged from broader consensus 

• Monitoring of rating volatility and migration over time 

• Regulatory and governance expectations around model validation 

• Alignment between first- and second-line risk functions 

How Credit Benchmark Was Used 

• Model Validation: Consensus ratings were incorporated into model performance reviews as an independent benchmark. 
• Peer Benchmarking: Internal ratings were compared with anonymized peer bank consensus to investigate meaningful differences. 
• Portfolio Monitoring: Historical rating movements helped identify obligors and sectors requiring closer review. 
• Governance: Consensus data provided additional evidence supporting internal governance discussions and model oversight. 
• Business Engagement: Independent benchmarking helped explain internal credit assessments across the organization. 

Business Outcomes

Our client has found Credit Benchmark data particularly transformative in areas such as project finance and investment funds, where conventional ratings are often sparse or absent. The depth, breadth, and objectivity of consensus-based insights have delivered measurable value in:  

• Stronger Model Validation: Independent consensus data provides additional support for model validation and governance activities.
• Improved Risk Oversight: Historical rating movements and peer comparisons provide additional context for monitoring portfolio risk.
• More Focused Analysis: Differences between internal ratings and peer consensus help identify obligors requiring further review.
• Greater Organizational Confidence: Independent consensus data supports alignment between first- and second-line teams and reinforces confidence in internal credit decisions.

Why It Matters

For sophisticated financial institutions, Credit Benchmark is not a replacement for internal models—it is an independent reference point. Its consensus, derived from the internal credit assessments of leading global financial institutions, provides additional perspective that supports model governance, strengthens portfolio oversight and helps risk teams make better-informed credit decisions.