Private credit is now a core pillar of institutional portfolios.
The market has grown from roughly $2 trillion in 2020 to $3 trillion in 2025, with projections suggesting it could reach as much as $5 trillion by 2029.
As the asset class scales, investors are looking for clearer signals on market health, from leverage multiples and spread compression to borrower resilience in a higher-rate environment.
New direct lending benchmarks give private credit investors the type of reference point that public market investors have long relied upon. Indices such as the Kroll StepStone private credit benchmarks, the Cliffwater direct lending index and the Lincoln senior debt index track leverage, spreads and EBITDA margins across large transaction datasets..
Greater transparency should strengthen markets. Yet in private credit, how benchmarks are constructed matters as much as the data they display.
Public Anchors Vs. Private realities
In public markets, benchmarks underpin pricing and performance measurement. Securities trade continuously, prices are observable, and index methodologies are widely understood.
Private credit operates under different conditions. Transactions are negotiated bilaterally and deal terms are often confidential. Reporting is periodic and can vary significantly between managers, with valuations typically model-based rather than determined by an exchange. Even core metrics such as leverage and earnings adjustments may be defined differently across firms and strategies.
These structural differences make direct comparisons with public market benchmarks inherently challenging. In a higher-rate environment, visibility into leverage trends and margin compression is valuable, particularly when investors are trying to distinguish genuine manager outperformance from broader market dynamics. Yet the presence of a benchmark does not automatically eliminate opacity. Useful benchmarks depend on the data that underpins them. And in private credit, that data is rarely standardized.
Self-Reported Data Isn’t Enough
Without methodological clarity, benchmarks risk creating a false sense of precision. Construction choices can materially influence outcomes. Datasets weighted toward sponsor-backed loans may not reflect asset-based or non-sponsored lending. Larger managers contributing disproportionate data may shape aggregate trends in ways that are not immediately visible. If those biases are not disclosed, investors may draw conclusions about leverage, spreads or risk that do not fully reflect the broader market.
Constructing meaningful indices requires accurate, standardized and independently validated information. Investors should look for indices that clearly disclose their methodology and underlying data sources, allowing them to assess whether the benchmark meaningfully reflects the segment of the market they are investing in. Investors should be asking four key questions:
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Is the data self-reported or externally verified?
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How are definitions harmonised across sectors and deal types?
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How frequently is information updated?
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What proportion of the market is actually captured?
The Retail Test for Private Credit
As private credit expands into retail-oriented and semi-liquid vehicles, the transparency stakes rise. After several years of strong inflows, investors are testing the assumptions built into semi-liquid fund structures Industry surveys show growing demand for periodic redemption features in private credit funds.
Executives across the market have warned that if inflows weaken further, redemptions, typically capped at a fixed percentage of net asset value each quarter, could begin to outpace new subscriptions at certain funds.
When inflows decelerate, periods of heightened redemption requests can expose the operational and valuation complexities inherent in managing semi-liquid formats.
Private credit has long emphasised that it avoids classic liquidity mismatches because long-term capital is paired with long-term assets. That structural alignment remains central to the asset class. As strategies expand into retail-oriented and semi-liquid vehicles, transparency around portfolio composition, valuation methodologies, exposure concentrations and liquidity buffers becomes even more critical.
Benchmarks may enhance comparability, but they cannot substitute for structural clarity. When liquidity dynamics, valuation practices and redemption mechanics differ across vehicles, understanding the foundations of the data becomes essential to interpreting headline metrics.
Why Private Credit Needs Stronger Data Foundations
No single index can fully capture the heterogeneity of private markets across geographies, borrower profiles, capital structures and risk appetites. To sustain investor confidence as it grows, private credit must extend beyond publishing headline leverage and spread metrics. Clear data sourcing, consistent definitions across managers, transparent methodologies and independent validation processes will be central to ensuring that benchmarks function as meaningful market barometers.
In practice, that means moving toward greater standardization in how core credit metrics are defined and reported across the industry. Common frameworks for calculating leverage, EBITDA adjustments and covenant structures would allow datasets from different managers to be compared more reliably.
Greater use of third-party administrators and independent data validation could also strengthen confidence that benchmark inputs are consistent and verifiable rather than purely self-reported. In reality, even among sophisticated managers, it’s not uncommon to see similar loans reported with meaningfully different leverage or earnings adjustments depending on internal methodologies.
This is where technology and operational infrastructure become decisive. At Carta, we see how standardizing and validating data at the source not only improves benchmarking, but helps build the data infrastructure required for more advanced use cases, including AI tools that depend on structured, consistent inputs.
Private credit has entered a more mature phase. Benchmarks are a natural step forward. The question now is whether the industry will embrace the data infrastructure to make them mean something, or settle for the appearance of transparency.