The comparability gap
Private credit underwriting is rigorous at the deal level. Sponsor materials, lender models, internal scorecards, and analyst overlays produce a detailed view of each borrower. The challenge, however, is not analytical depth. It is expressing those views in a form that can be aggregated and compared consistently across portfolios, communicated in a standardized way to all the parties to a private credit deal, and used downstream for activities like valuation and asset selection.
Credit assessments across different lenders in private credit are heterogeneous. Leverage and EBITDA are defined and adjusted differently across underwriters (and transactions). Structural protections sit in documentation rather than in reported metrics. Internal scorecards vary in depth and interpretation. None of this reflects a lack of discipline; it reflects the bespoke nature of the asset class and the organic, organization-specific methodologies. But it means that relative credit quality across portfolios is often inferred through implicit normalization rather than expressed through a consistent framework, and this gap widens as portfolios scale across vintages, geographies, and originators.
The need for a scalable alternative
Agency credit ratings are one established mechanism for achieving comparability. A rating expresses a borrower's credit risk in a standardized ordinal scale, enabling cross-portfolio comparison and a common language among deal participants. However, a large segment of the private credit market is unrated and is likely to remain so. For these exposures, investors need an alternative that delivers some of the same benefits of standardization and comparability, but that can also scale across hundreds or thousands of positions.
Existing tools address parts of this gap. Probability-of-default models provide granular, point-in-time default risk estimates, but their outputs are horizon-specific and sensitive to short-term movements in fundamentals, making them less suited to the through-the-cycle perspective that characterizes most private credit portfolios. Internal scorecards capture deal-specific nuance, but are resource-intensive, may be based on stale financials, and structurally non-uniform when applied across large books. What is missing is a measure that occupies the middle ground: model-driven, standardized, ratings-comparable, timely, and scalable.
A practical risk assessment framework for private credit must also reflect the constraints under which portfolios are actually managed. It must often operate with limited and non-standardized data, recognizing that borrower disclosures are often incomplete, adjusted, or inconsistent across transactions. Despite these challenges, it must produce outputs that are comparable across borrowers, regardless of how those borrowers were originated or underwritten. It must be sufficiently stable to support cross-sectional analysis, rather than being dominated by short-term volatility. At the same time, it must remain interpretable, so that outputs can be understood, challenged, and incorporated into investment and risk discussions.
Crucially, it must integrate into existing processes. Private credit does not lack analytical depth; it lacks a common structure through which that analysis can be interpreted consistently across a portfolio, communicated, and. Any additional framework therefore needs to complement, rather than disrupt, established underwriting, monitoring, and reporting practices.
EDF-X CreditGradient: a scalable, model-based ratings-comparable risk measure
Moody's EDF-X CreditGradient is designed to fill this much-needed gap. It is a model-based, ratings-comparable risk measure that provides a standardized expression of credit quality for unrated exposures, one that investors can use to help achieve consensus on the relative risk of each position in a portfolio.
The model relies on a set of core drivers underlying fundamental credit assessment, including scale, leverage, profitability, probability of default (PD), and firmographic factors such as sector and geography, to generate a risk measure expressed in Moody’s long-term ratings scale A key design feature is that the model operates entirely on quantitative inputs. Unlike traditional scorecards or internal frameworks that depend on qualitative overlays, CreditGradient is able to produce a robust and comparable measure of credit quality without requiring subjective inputs. This is important in practice, as the need for qualitative information often limits scalability and consistency across portfolios.
Based on an supervised learning AI/ML methodology, focused on the relationship between financial profiles and observed rating outcomes, the model is able to capture the structural drivers of credit quality in a way that remains both scalable and interpretable. The result is a through-the-cycle oriented measure of credit quality that allows exposures to be positioned consistently relative to one another, regardless of how they were originated or underwritten.
By design, the CreditGradient output is expressed in a ratings-comparable format: credit ratings are the lingua franca of credit risk. Expressing model output on a ratings-equivalent basis means that the measure can be immediately understood, discussed, and acted upon by every participant in a private credit transaction, from portfolio managers and risk committees to co-lenders, CLO trustees, and limited partners. It transforms an opaque, deal-specific risk assessment into a transparent, standardized signal that supports consensus-building across stakeholders.
This communication function is not secondary to the analytical one. It is integral. A risk measure that cannot be efficiently communicated to stakeholders has limited practical value, regardless of its technical sophistication. CreditGradient is designed with this reality in mind: its output is not just analytically useful, but operationally communicable.
Illustrative application
Consider two sponsor-backed borrowers operating at similar leverage levels with comparable interest coverage. At the deal level, they may differ materially in scale, sector exposure, and earnings stability, differences that are typically well understood by the underwriting team but difficult to express on a comparable basis across the portfolio.
CreditGradient resolves this by incorporating scale, profitability, PD, and sector context alongside leverage within a single structure, producing a ratings-comparable output that allows the two exposures to be positioned relative to each other and to the rest of the book. The objective is not to override the underlying credit view, but to express it in a form that is consistent and comparable.
Integration into the investment workflow
At origination, CreditGradient supports initial screening by providing a common basis for comparing new opportunities with existing portfolio exposures. This is particularly valuable in high deal-flow environments where efficient prioritization matters more than full underwriting at the earliest stage.
Within the portfolio, the same framework supports internal risk tiering, grouping exposures consistently regardless of how they were originally underwritten. This produces a more structured and transparent view of how credit quality is distributed across the book.
The model can be refreshed on a regular basis to provide an updated view of underlying credit quality, without requiring new financial statements at each point in time. This reflects a distinguishing feature of the approach: by incorporating a point-in-time PD measure, CreditGradient is able to generate monthly updates even between reporting periods, when financial disclosures may be infrequent. The emphasis is therefore not on reacting to each set of new financials, but on maintaining a stable, comparable perspective over time, where changes in credit quality can be assessed consistently in a portfolio context rather than in isolation.
In governance settings, factor-level drivers enable structured discussions. Credit views can be anchored in a common analytical framework, improving the clarity and consistency of internal communication as well as reporting to external stakeholders.
Portfolio-level implications
A standardized, ratings-comparable measure does not change how individual credits are underwritten. Its impact is realized at the portfolio level. By expressing credit quality through a common structure, it becomes possible to compare exposures across vintages, strategies, and geographies on a like-for-like basis, reducing reliance on implicit normalization and making portfolio positioning explicit.
The distribution of credit quality becomes clearer, as do risk-based valuation measures, supporting more deliberate portfolio construction and facilitating the identification of concentrations. Monitoring becomes more systematic. And because the framework relies on a parsimonious set of standardized inputs, it scales without imposing additional data requirements. This is a critical consideration in private credit, where the availability and consistency of financial information varies materially across borrowers.
Conclusion
As the private credit market continues to scale, the limiting factor is not analytical capability. It is consistency of interpretation and communication. Much of the market is unrated and will remain so. Investors need a measure that is standardized, comparable, scalable, and expressed in a language that all deal participants understand.
CreditGradient addresses this need. It provides a repeatable, model-based, ratings-comparable risk measure for unrated exposures, one that complements rather than replaces underwriting, judgement, or existing models. It gives investors a credible basis for achieving consensus on the credit risk of every position in a portfolio, and a standardized signal that can be communicated efficiently to every stakeholder in the transaction.
The result is a shift from fragmented, deal-level assessments toward a coherent and scalable framework for interpreting and communicating portfolio risk.