Private credit has evolved from a niche alternative into one of the fastest-growing asset classes in global finance. With assets under management exceeding $1.7 trillion across the U.S., Europe, and Asia, it occupies a position between public credit markets and traditional bank lending. Its rise has been fueled by banks’ retrenchment after the global financial crisis, investors’ search for yield in a low-rate environment, and the desire for diversification beyond public markets.
Private credit carries additional risks – illiquidity, opacity, borrower concentration, and bespoke structures – that distinguish it from corporate bonds and bank loans and complicate its evaluation and oversight.
Direct lending, the core of private credit, is typically structured with shorter maturities of five to seven years and floating-rate features that reduce sensitivity to interest rate volatility. Investors are further compensated with illiquidity premiums of 300 to 500 basis points above comparable public debt. The underlying assumption is that this extra yield compensates investors not only for liquidity and transparency challenges but also for credit, structural, and systemic risks.
Alla Gil
However, whether this premium is sufficient depends on how these risks play out across a wide range of macroeconomic and market scenarios. In periods of stable growth and modest credit losses, the additional spread can indeed enhance portfolio returns. But in more stressed environments characterized by higher default rates, valuation opacity, or systemic liquidity freezes, the premium may fall short of covering cumulative losses and spillover effects.
Numerous regulatory bodies, research institutions, and market participants have issued warnings about the risks embedded in this asset class. Industry experts warn that default rates and financial distress in private credit are being masked or underreported due to loans restructuring, maturities extensions and cash payments being replaced by payment-in-kind (PIK) loans.
Scenarios and Their Calibration
This makes rigorous risk evaluation essential. That requires scenario testing, portfolio-level stress analysis, and close scrutiny of structural protections such as covenants, collateral coverage, and sponsor backing. Ultimately, the critical question is whether the promise of higher returns is enough to endure the full spectrum of risks inherent to private credit including the unprecedented ones.
To estimate tail risk in private credit, one could use some techniques that have been developed for bank loans. There are substantial differences between these asset classes. Private credit is intended for the riskier companies that banks might not lend to. So it encompasses institutional non-bank lending outside public markets, often executed by asset managers, debt funds, or business development companies (BDCs).
Bank loans are broader, including personal, small business, or C&I (commercial and industrial) loans extended by banks or credit unions.
Yet there are many features in common. Most of private credit and bank loan instruments have floating-rate structures. They have first-lien seniority and five- to seven-year maturity terms. So it is possible to apply some of the techniques – like extracting default and downgrade probabilities and loss given default (LGD) from market observations – developed for bank loan portfolios to private credit portfolios, but with the modified calibration process.
Credit spreads that are charged for direct lending are observable and represent expected loss. LGD component can be implied from collateral required in the direct lending transaction by subtracting potential recovered value from the total loan amount. So even without public or internal rating (used in traditional bank lending), one could extract the implied probability of default (PD).
As observable credit spread contains other components besides pure default risk, e.g. illiquidity premium, delinquency risk, etc., it can be separately analyzed given historical time series of bank loans and direct lending instruments of similar credit quality.
Components of Credit Spreads
To estimate each component of direct lending risk, let’s start with expected loss (EL), that is equal PD times loss given default (LGD), EL = PD * LGD. Both components are scenario dependent and not directly observable. But they can be implied from the observable market variables, like credit spreads that are charged in direct lending.
As these credit spreads (S) are substantially wider than similar quality bank loans or corporate bonds, the difference should be attributed to all these extra risks of private credit, like illiquidity, opaqueness, etc. So S = SD + SL, where SD represents the spread charged for default and deterioration risk, while SL is charged for additional risks of private credit.
Keeping in mind that LGD is driven by the seniority of the obligation and depends on the value of the assets underlying the collateral, one can evaluate the total recovery in the event of default. As direct lending has first-lien seniority, one can estimate what share of the full recovery value will go to a private lender, thus estimating the LGD of a respective instrument.
Portfolio-Wide Analysis
This enables approximating the value and cash flows not just of a single private credit loan, but the entire portfolio of such loans. Moreover, it will be consistent with any other instruments or asset segments in the overall investment portfolio.
This is achieved by implementing the following steps: (1) generating the full range of scenarios with macroeconomic and market variables, shock events, and their consequences triggering changing correlations; (2) projecting all components of private credit loans on all scenarios; and (3) estimating mark-to-market (MTM) and cashflow of each loan and the entire portfolio.
Step (1) will generate implied dynamic correlations where all SD spreads can widen simultaneously, causing not just increase of probabilities of defaults and downgrades for each loan, but the copula correlations between such events. At the same time, asset prices and values of collateral go down across the entire portfolio in stressful scenarios, causing lowered recoveries and increased LGDs. So implied correlations between PDs and LGDs will naturally increase in these stressful scenarios exactly as observed in real life.
Step (2) involves connecting the second spread component, SL, charged for illiquidity and opaqueness to the macroeconomic and market variables using ML-based regression with regularization.
And step (3) allows us to put all these components together for risk-neutral evaluation of MTM on all real-world scenarios at each point in time and respective cashflows consisting of interest payments, matured amounts and realized after-default recoveries.
Parting Thoughts
Private credit’s growth story is impressive, but its risk profile is more complex than many headlines suggest. What’s hidden is not necessarily sinister – but it can be expensive if ignored.
As private credit moves from alternative to mainstream, investors must upgrade their risk toolkits, question the assumptions of past cycles, and remain skeptical of “stable” returns that are not stress-tested. The hidden risks are real, but the described method to uncover them will help analyze whether hidden tail risks are well enough covered by the yields.
Alla Gil is co-founder and CEO of Straterix, which provides unique scenario tools for strategic planning and risk management. Prior to forming Straterix, Gil was the global head of Strategic Advisory at Goldman Sachs, Citigroup, and Nomura, where she advised financial institutions and corporations on stress testing, economic capital, ALM, long-term risk projections and optimal capital allocation.