Private Credit’s Verification Problem

Private credit faces a fundamental verification and information problem. Recent market developments have brought these issues into sharper focus. As liquidity tightens, and redemption pressures increase, private markets are undergoing what appears to be a structural test rather than a cyclical slowdown. Years of capital accumulation in semi-liquid structures are now colliding with more constrained liquidity conditions, exposing tensions between asset valuation and the ability to realize those valuations.

The misalignment between fund managers and investors is evident in the persistent discounts seen in business development companies (BDCs) relative to reported net asset values (NAVs). These discounts reflect credit risk, liquidity, and market conditions, but they also signal that investors are applying a discount when they cannot fully interpret or validate model-based valuations against market pricing. These discounts reflect credit risk, liquidity, and market conditions, but also highlight the gap between model-based valuations and market pricing—particularly when investors attempt to infer value from non-traded assets.

Private credit lacks comparable public market mechanisms—continuous price discovery, mandatory disclosures, and standardized auditing—that provide transparency and external validation. As a result, investors have limited ability to independently verify how valuations are constructed.

Verification does not make valuation assumptions correct, but it does make them transparent, reproducible, and open to scrutiny. In a market where key inputs remain judgment-based, improving verifiability does not eliminate uncertainty, but it can reduce ambiguity around how valuations are constructed.

This post examines how a combination of approaches, including statistical data screening, cryptographic proof, and stress testing, can improve different aspects of the verification process and strengthen confidence in private credit valuation.

Private credit’s structure can be understood as a supply chain of intermediaries: origination, structuring, valuation, and distribution. Each stage relies on the outputs of the previous one, often without independent validation. As a result, uncertainty is not resolved at any single point but carried forward, accumulating across the system and embedding itself in how risk is priced.

Periods of market stress make this dynamic more visible. When liquidity is abundant, uncertainty can remain latent. But as redemption pressures rise, the absence of independent validation becomes more consequential. What appears as a liquidity problem is also a verification problem—investors are not only seeking to exit, but to determine what those assets are actually worth under current conditions.

This dynamic is consistent with Akerlof’s “market for lemons” (1970), extended to opaque asset markets by Ivashina and Sun (2011) and Chernenko et al. (2022). When one party to a transaction holds materially superior information that cannot be independently verified, rational counterparties apply a discount.

In this context, BDC discounts reflect not only concerns about credit quality, but also the difficulty of validating the assumptions underlying reported NAV. The inability to distinguish between stronger and weaker valuations leads investors to price in uncertainty more broadly.

Private credit valuations follow a familiar structure: NAV reflects loan balances plus accrued interest, less expected losses. Expected losses are typically calculated using probability of default (PD), loss given default (LGD), and exposure at default (EAD). But while the framework resembles that of public markets, the inputs are fundamentally different.

In private credit, PD, LGD, and EAD are largely model-driven and internally assigned rather than market-implied. Default probabilities are based on internal ratings or rule-of-thumb mappings rather than traded spreads; recovery assumptions depend on collateral valuations that may be infrequent or judgment-based; and exposure estimates incorporate undrawn commitments that require further assumptions.

As a result, NAV is constructed from an assumption stack rather than anchored to observable market signals. In periods of stress, those assumptions are implicitly tested, as investors seek to determine whether modeled values align with realizable outcomes. Expected loss becomes a primary driver of valuation rather than an output inferred from price.

This shifts the problem from valuation alone to verification. Two distinct questions emerge: whether the calculation is performed correctly, and whether the inputs accurately reflect the underlying loans. Verification frameworks can address the former and improve transparency around the latter, but they do not fully resolve it.

This distinction becomes more important in periods of stress, when assumptions around default, recovery, and exposure are more likely to be challenged.

Verification in private credit operates across three layers:

• Data integrity: Are the inputs internally consistent?
• Computation integrity: Was the valuation calculated correctly?
• Risk visibility: How does the portfolio behave under stress?

Different tools address each layer. Cryptographic methods are most effective at the computation level, while statistical screening and stress testing address complementary aspects of the problem.

Cryptographic techniques are already embedded in financial markets, securing payments, digital signatures, and, increasingly, distributed ledgers. These methods ensure that data remains unaltered and can be independently verified without reliance on a single intermediary.

In private credit, cryptographic hashing serves as a commitment mechanism: it binds a party to a specific computation at a given time. A hash functions like a digital fingerprint—unique to the data, with even small changes producing entirely different outputs. A SHA-256 hash of inputs and outputs creates a fingerprint of the full calculation, allowing third parties to confirm that results were produced from a specific set of assumptions and have not been altered.

A Merkle tree extends this approach to structured datasets by organizing data into a hierarchy of hashes, producing a single root hash that represents the entire dataset. Any change to the underlying data alters this root hash, making tampering immediately detectable while allowing selective verification.

Blockchain anchoring adds a timestamped attestation layer. By recording the root hash on a public ledger, a fund creates an immutable record of when a computation occurred, independent of internal systems.

These tools address the computation-level verification problem. They ensure that valuations are reproducible and consistent, but they do not determine whether the underlying assumptions are correct.

Before computation can be verified, the integrity of the underlying data must be assessed. Statistical methods provide a practical, though imperfect, approach.

Benford’s Law predicts how digits appear in naturally occurring datasets. Deviations from this pattern can signal manipulation or synthetic data. In private credit, it can be applied to loan balances, collateral values, and interest rates.

Additional checks include detecting collateral reuse across borrowers, inconsistencies between Debt Service Coverage Ratios (DSCR) and payment status, clustering of round numbers, and anomalies in borrower identifiers.

More recently, LLM-assisted techniques have emerged to detect subtler inconsistencies, including synthetic data patterns and narrative mismatches.

These methods do not establish ground truth. They act as triage tools, identifying where further scrutiny is warranted. Their importance increases in periods of stress, when data assumptions are more likely to be challenged.

While data and computation address how valuations are constructed, stress testing addresses how they behave under adverse conditions.

Monte Carlo simulation models potential outcomes using correlated assumptions across defaults, recoveries, and interest rates. Treating these variables as independent understates tail risk.

Simulations produce a distribution of NAV outcomes, from which metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) are derived. CVaR is particularly relevant in private credit, as it captures the magnitude of extreme losses.

In the current environment, where default expectations are rising and sector pressures are emerging, the importance of these correlations becomes more pronounced.

In this sense, stress testing becomes a proxy for price discovery, helping investors understand how valuations might behave under conditions where market signals are absent.

Verification improves transparency, but it does not eliminate uncertainty.

At the computation level, cryptographic methods ensure reproducibility, but not correctness. Model risk remains.

At the data level, statistical screening can identify anomalies but cannot confirm accuracy. External validation remains complex.

At the risk level, stress testing depends on assumptions that may not hold in extreme scenarios.

Finally, verification does not resolve the structural gap between mark-to-model valuations and realizable prices. In stressed markets, this gap becomes more visible.

Efforts such as ICE’s private credit data platform represent early steps toward standardization. But key questions remain around methodology, proof standards, and monitoring.

Public markets offer a useful precedent. Generally Accepted Accounting Principles (GAAP) standardized financial statement inputs, while the Public Company Accounting Oversight Board (PCAOB) standardized auditing method. Both were necessary to establish comparability and credibility.

Given the scale of the asset class and increasing scrutiny from institutions such as the Financial Stability Board and the Financial Accounting Standards Board broader adoption of verification frameworks appear likely.

Market stress has underscored what is at stake. When liquidity tightens and assumptions are questioned, investors are effectively forced into a form of price discovery—seeking to determine what assets are actually worth in the absence of observable prices.

Improving verification will not eliminate uncertainty or prevent losses. But it can make valuations more transparent, consistent, and comparable—and reduce the ambiguity that investors are currently pricing.

For private credit, the question is whether verified NAV will evolve into a regulatory standard or remain a differentiator for early adopters. The answer will shape whether verifiability becomes a competitive advantage or a baseline expectation across the industry.