Research — January 23, 2026

Build, Buy, or Bridge? Benchmarking and Analytical Frameworks for Private Credit

Highlights

  • Institutions strategically choose whether to build proprietary models or buy off-the-shelf solutions. Now they also have an option to “bridge” both approaches when scaling credit analytics.
  • Robust benchmarking has become a key differentiator, empowering traditional and alternative lenders to validate risk models, improve transparency, and align with market expectations.
  • Adopting hybrid analytical frameworks strengthens governance, supports scalable portfolio management, and enables firms to adapt to evolving regulatory and market demands.

Credit analysis, as a practice, dates back to ancient times. Rudimentary lending existed in Mesopotamia and Ancient Rome, where early creditors made basic assessments of borrower trustworthiness. The academic foundations of modern credit scoring emerged much later. In the 1940s, statistician David Durand published pioneering research on distinguishing “good” and “bad” loans. In his book Stable Chaos, Durand noted: “Systematic procedures and objective tests serve to strengthen the analyst’s judgment, not to replace it; they enable him to learn more quickly and more effectively from his own experience, and to sharpen his critical faculties.”

It took another couple of decades for these ideas to evolve into commercially successful credit risk scoring solutions. Today, it is difficult to imagine any lender operating without robust credit risk models powered by reliable data. And the future of credit risk innovation promises to be as dynamic as the past six decades, particularly with the rise of agentic and artificial intelligence frameworks that are rapidly becoming tools on every analyst’s desk.

Yet many institutions still struggle with foundational challenges: insufficient data and expertise in specialised or opaque asset classes, and responding to the growing regulatory scrutiny, to name a few. In private credit, these frictions are compounded by liquidity considerations, valuation complexity, concentrated exposures, and competitive pressures.

Against this backdrop, investors and lenders are increasingly confronted with a strategic question about their analytical capabilities: “Build, Buy, or Bridge?”

This article explores how model benchmarking serves as the decision-making anchor for that choice and why it is fast becoming a defining capability for private credit players scaling their portfolios.

The Private Credit Boom: Opportunity Meets Analytical Strain

Recent S&P Global Ratings research shows how quickly private credit has transformed from a niche alternative into a broad, multi-strategy ecosystem spanning mid-market corporates, fund-finance vehicles, and infrastructure. This rapid expansion has reshaped the market into a more fragmented and less liquid environment, where bespoke structures and covenant-lite terms leave lenders with far less room for analytical error.

At the same time, the pressure to deploy capital swiftly is intensifying. Deals are more negotiated, structures more complex, and supervisors are signalling higher expectations for governance and analytical discipline across nonbank lenders. LPs, too, are demanding more transparency and evidence that platforms can measure and manage risk in a consistent, repeatable way.

Analytically, private markets are inherently challenging. Default histories are sparse, financial disclosures inconsistent, and borrower profiles are highly heterogeneous. Many younger platforms operate with limited internal data infrastructure, forcing analysts to make judgment-heavy decisions with incomplete information. These dynamics raise the stakes around how institutions choose to design and scale their analytical frameworks.

This is where benchmarking becomes especially critical. With fewer natural market reference points, investors need external anchors to contextualise performance, understand relative risk, and compare exposures across an increasingly diverse opportunity set. This isn’t theoretical. In one recent case, an investment firm used benchmarking and transparent analytics to strengthen investor confidence, demonstrating that robust benchmarking is now a commercial differentiator, not a technical exercise (see the Case Study at the end of this article).

The Hidden Risk of Scaling Without Benchmarking

As platforms grow, the weaknesses of internal-only models tend to compound. Limited data and fast origination cycles could make simple models a necessary starting point, but without external comparison points, they can drift quickly. As sparse defaults create unstable PDs and ratings, analyst judgment begins to dominate. Systematic biases - whether conservative or aggressive - go undetected until they show up in mispricing or lost deal flow.

We saw this firsthand with a mid-sized lender mentioned in an earlier article: high-quality borrowers were being overestimated on risk, leading to uncompetitive loan pricing. Benchmarking revealed the root cause, enabled recalibration, and helped the lender realign with market expectations.

This is why benchmarking is fast becoming the assurance standard in private credit, too. External references help institutions validate discriminatory power, detect drift early, and price risk with greater confidence. They also create the evidence that investors and LPs increasingly expect.

With these foundations in place, institutions face the strategic question at the heart of private credit analytics today: How should we scale: by building, buying, or bridging our analytical capabilities?

“Build” When Internal Capability Becomes a Strategic Asset

When does building proprietary credit models truly make strategic sense? The clearest case is well understood. Regulated institutions adopting the Internal Ratings-Based (IRB) approach under Basel need internal models. But the rationale extends far beyond regulatory mandates. In practice, strong in-house models form the analytical backbone for lending decisions, valuation processes, portfolio monitoring, accounting loss provisions, and competitive loan pricing. Without them, institutions often find themselves improvising at the very points, where the precision matters most.

The industry has seen this play out before. During the rollout of IFRS 9 seven years ago, even IRB banks with established modelling frameworks faced significant implementation challenges. Institutions operating under the Standardized Approach, many of which lacked robust internal models, struggled even more, and many continue to refine their Expected Credit Loss methodologies today. The lesson was simple: without a solid modelling foundation, new regulatory or accounting standards expose analytical gaps rapidly.

Advantages

When conditions are right, building internally offers meaningful strategic upside:

  • Full ownership and transparency into methodologies, assumptions, and governance.
  • Models tailored precisely to the institution’s portfolio characteristics, underwriting style, and historical experience.
  • Ability to embed idiosyncratic data or qualitative insights that off-the-shelf solutions cannot easily replicate.

For mature private credit platforms with consistent annual deal flow, these benefits translate into tighter risk differentiation, more confident pricing decisions, and analytics that evolve organically with the business.

Challenges

These advantages come with real demands which are often underestimated:

  • Specialised talent is essential: data scientists, credit modellers, quants, validation teams, and credit officers as the business subject matter experts.
  • Technical infrastructure, even with open-source tools like R and Python, must be reliable, secure, and scalable.
  • Data collection and management is the heaviest lift; assembling, cleaning, and structuring input data requires organisation-wide coordination.
  • Extended development cycles are typical, especially when governance, validation, audit, and documentation are factored in.

As a result, proprietary model development is most effective when carried out by institutions with the scale, stability, and long-term orientation to sustain ongoing investment, not as a one-off project, but as a living analytical capability.

“Buy” off-the-shelf scorecards or models

The natural counterpart to building internally has long been vendor-supplied scorecards and quantitative models. These solutions typically leverage large, pooled datasets, often contributed by lenders and supplemented with external sources, and apply established statistical, structural, or ratings-based methodologies. Centralised development allows vendors to deliver broadly validated tools that can be deployed quickly.

Advantages

  • Rapid time-to-market with a functioning, structured analytical framework.
  • Immediate consistency and standardisation across underwriting decisions.
  • An essential starting point for institutions lacking sufficient internal data, modelling teams, or deep quantitative expertise.
  • Supports early data capture and progressive refinement,  enabling adjustment across a wide range of sectors, exposure types and portfolio strategies, either internally or in partnership with a specialist provider.

As long as data representativeness can be demonstrated and regulatory expectations around vendor models and validation are met, off-the-shelf solutions offer a credible and efficient entry point. Unsurprisingly, lenders prioritising speed, consistency, and standardised underwriting have gravitated toward this option, echoing Durand’s emphasis on systematic procedures and objective tests.

Challenges

  • Customisation is rarely free: recalibration and structural adjustments are often needed to reflect portfolio specifics or bespoke deal features.
  • Ongoing reliance can effectively outsource validation, back-testing, recalibration, and maintenance to the vendor.
  • Data freshness and continuity may suffer over time, particularly where models depend on voluntary data contributions.
  • As institutions build internal capabilities, regulators’ demands for transparency can reduce willingness to contribute data, weakening pooled datasets.

While off-the-shelf models offer speed and structure, they may not always fully align with an institution’s unique portfolios, bespoke deals, or evolving data capabilities. Furthermore, acquiring the model is only part of the solution, institutions still need skilled resources experienced in analysing the credit sensitive assets and implement the tools correctly. That creates a space for an alternative approach, “Bridge”, where organisations combine the immediacy of vendor solutions with selective development, calibration, and expert oversight. This middle path allows firms to scale efficiently while retaining flexibility, control, and alignment with their strategic objectives.

The “Bridge” Approach– Benchmarking via Challenger Models and Credit Risk Assessments

The bridge option sits deliberately between build and buy and typically takes one of two closely related forms.

  • External credit risk assessments: engaging a reputable third party with a recognised methodology and experienced credit professionals to support underwriting during portfolio ramp-up.
  • Benchmarking via challenger models: retaining internal underwriting while systematically comparing internal model’s design and outputs to an external reference model (or outputs from it) to assess consistency, calibration, and bias.

Both approaches are used by institutions at different stages of maturity: early-stage platforms building their first analytics capability, as well as established managers seeking additional assurance around model deployment, performance, and governance.

Advantages

  • Instant access to experienced credit analysts, quants, and economists without committing to large, fixed teams.
  • Operational flexibility as origination volumes scale up or down.
  • Early introduction of benchmarking and model assurance, even before sufficient internal performance data exists.
  • Stronger credibility with stakeholders through demonstrable discipline and external reference points.

Crucially, bridging does not outsource judgment. Institutions retain ownership of credit decisions and governance while outsourcing capacity, infrastructure, and analytical depth.

Challenges

  • Requires clear governance, well-defined roles, and explicit accountability.
  • Transparency into methodologies and assumptions must be actively managed.
  • Institutions must retain decision ownership, ensure transparency into methodologies, and maintain strong oversight
  • Without oversight, there is a risk of over-reliance on external judgment.

When implemented thoughtfully, the bridge model is particularly effective for small and mid-sized managers, new entrants, early fund vintages, new asset classes, or organisations with uneven origination cycles. It allows firms to professionalise analytics from day one, avoid the hidden risks of scaling too quickly on internal-only models, and preserve strategic optionality.

In many cases, bridging is not an endpoint but a controlled pathway either toward internalisation as scale, data, and confidence grow, or toward formalising a “buy” decision once an external methodology has been sufficiently tested, calibrated, and proven fit for purpose.

The graphic below shows a stylized decision framework institutions can reference that summarizes the above considerations.

Figure 1) Decision Framework

Decision Framework Scorecards

Conclusion

There is no single “right” answer to the build, buy, or bridge question in credit. The optimal approach depends on scale, portfolio complexity, origination cadence, data maturity, and strategic ambition. In practice, the most effective solution is often not one model, but a combination of approaches operating side-by-side.

As investment strategies evolve and portfolios diversify, institutions increasingly deploy off-the-shelf or in-house scorecards to cover the majority of exposures, while supplementing niche segments or unique deal structures with targeted external credit assessments. This hybrid strategy enables managers to maintain consistency and efficiency across their core portfolio, while retaining the flexibility to respond to areas where existing model coverage is imperfect or the appetite to onboard an entirely new model is low.

Over the lifecycle of a fund, such parallel frameworks allow managers to adapt to changing investment priorities, market conditions, and regulatory expectations—without forcing premature or irreversible analytical commitments.

As David Durand famously noted, relying on a single metric is like assuming a hungry gourmet would be satisfied with only pickled herring, it ignores the full richness of the data available. In private credit today, this insight reinforces the case for benchmarking and methodological pluralism: no single model can stand alone without disciplined comparison, calibration, and contextual understanding.

S&P Global Market Intelligence uniquely supports private credit managers across all three paths. We work with institutions building internal capabilities, provide externally validated benchmarks and methodologies for those buying solutions, and offer flexible benchmarking, challenger models, and credit risk assessments for firms pursuing a bridged approach. Across each model, the objective is the same: to help institutions strengthen analytical discipline, improve comparability, and make better-informed risk decisions.

As private credit continues to mature, the managers that succeed will be those who treat analytics not as a constraint on growth, but as a strategic enabler of it. We invite market participants to engage with S&P Global to discuss their specific challenges and explore how benchmarking and analytical frameworks - whether built, bought, bridged, or blended - can support sustainable scaling in an increasingly demanding environment.

Case Study: A Global Private Credit Firm Elevates Risk Consistency and Analyst Alignment with S&P Scorecards

A leading global alternative credit manager with more than $150 billion in AUM sought to strengthen the consistency and objectivity of its credit underwriting. Although the firm already used a quantitative PD model, it wanted credit assessments clearly aligned with external rating standards, an expectation shared by its institutional investor base.

To support this, the firm adopted S&P Global’ Corporate, Project Finance, and Project Developer Scorecards, alongside Climate Credit Analytics, and engaged S&P Analytical Services to independently assess a number of borrowers across corporate and project finance portfolios. This independent review helped ensure the new scorecards were being applied correctly across diverse deal teams and mitigated the risk of “gaming” or inconsistent judgment in underwriting.

Through hands-on training, detailed factor-by-factor assessments, and iterative analyst dialogue, S&P Global helped refine internal assumptions, improve data collection, and enhance analysts’ understanding of key project and corporate risk drivers. The client reported that S&P Global expert-judgment approach, mapped to externally rated benchmarks, provided an additional layer of credibility that accelerated internal buy-in and strengthened governance.

The relationship has since expanded into new asset classes, with the client now pursuing a proof-of-concept for non-financial future-flow transactions.

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