Tokenized Private Credit LGD: What Default Actually Means

Why a tokenized credit pool is not the same thing as a credit card balance

Tokenized private credit is the on-chain wrapper around an old, unglamorous business: lending money to companies that cannot or will not borrow from a bank. Platforms like Ondo Finance (ONDO), the Centrifuge (CC) ecosystem, Hashnote (HASH) with its USYC product, and a growing list of permissioned pools accept stablecoin deposits and pass them, through a legal structure, to a portfolio of off-chain loans. The token you receive is a claim on that portfolio, priced daily at net asset value.

The framing matters because it explains both the appeal and the trap. The appeal is that private credit has historically delivered 8 to 12 percent gross yields to direct lenders, well above investment-grade bonds, with floating-rate coupons that adjust when central banks move. The trap is that those headline yields are gross of losses, and losses are precisely what the tokenized wrapper tends to obscure.

When a borrower in a private credit fund stops paying, four things happen, none of them on-chain. The loan is moved to a special-servicing team. Counsel is engaged. Collateral is valued and, if necessary, foreclosed on. And cash, if any is recovered, is paid back to lenders according to the waterfall in the credit agreement. This sequence can take 12 to 36 months. The token's NAV during that window is whatever the servicer says it is, marked either to a recovery estimate or to a distressed-sale price.

Loss given default, the number most yield hunters have never seen

Loss given default is the share of an exposure a lender does not recover when a borrower fails. It is the second half of the standard credit formula expected loss equals probability of default (PD) times loss given default (LGD) times exposure at default (EAD). In a consumer credit card book, LGD might run 60 to 80 percent. In middle-market private credit, historical recovery rates imply an LGD of roughly 40 to 60 percent, depending on seniority, collateral, and the cycle.

Worked example. Imagine a $10 million senior secured loan to a mid-sized industrial company. The loan has a first-lien mortgage on the borrower's factory, appraised at $14 million at origination. The borrower defaults after two years, having drawn the full $10 million plus $200,000 of accrued interest. Recovery takes 18 months. By the time the agent sells the factory in a workout, the real-estate market has softened and the equipment inside is obsolete. Net proceeds, after legal fees, servicer fees, and a 10 percent agent discount, come back at $5.5 million. LGD on the loan is roughly 45 percent: the lender lost $4.7 million on a $10.5 million exposure.

The reason LGD matters so much for token holders is that it compounds quietly. If a pool of 100 loans each has a 3 percent annual probability of default and a 50 percent LGD, the annual loss rate is about 1.5 percent. Against an 8 percent gross yield, that still leaves 6.5 percent. But the losses are not evenly distributed. In any given year, one or two borrowers drive most of the pain, and the recovery process for those specific names can take years, during which the token's NAV sits below par and secondary buyers demand a discount.

Senior, mezzanine, and junior tranches: who actually eats the loss

Most tokenized private credit pools are not a single homogeneous pool. They are structured as a stack of tranches, each with a different claim on cash flows. The waterfall in the loan documents, sometimes called the payment waterfall or distribution waterfall, dictates who gets paid in what order, both in normal operation and in default.

Senior tranches are paid first. In a normal quarter, they receive their coupon before anyone else. In a default, they have first claim on any collateral proceeds. A well-structured senior tranche might target an LGD of zero: it is engineered to be repaid in full even if multiple borrowers default, as long as the portfolio's collateral coverage test holds. This is the closest tokenized private credit gets to a money-market-like experience, and it is also where yields are lowest, often 5 to 7 percent.

Mezzanine tranches sit in the middle. They earn a higher coupon, typically 10 to 14 percent, in exchange for absorbing losses after the senior tranche is exhausted but before the junior tranche is touched. In a pool with $70 million senior, $20 million mezzanine, and $10 million junior, a $15 million loss would wipe out the entire junior layer, eat $5 million into the mezzanine, and leave the senior tranche untouched. Mezzanine holders would see their NAV drop by roughly 25 percent in that scenario.

Junior tranches, sometimes called first-loss, equity, or residual tranches, are paid last and lose money first. They exist precisely to absorb defaults so that more conservative tranches can advertise cleaner numbers. Junior tranches can target returns of 15 to 20 percent or more, but their distribution is bimodal: in good years they earn the full coupon, and in bad years they can be reduced to zero. When a tokenized pool advertises an attractive headline yield, ask which tranche that yield belongs to.

The gap between tokenized NAV and real collateral

On-chain wrappers make a fund look transparent because the token's NAV updates in near real time and the smart contract can even publish the underlying loan tape. In practice, NAV is only as accurate as the off-chain servicing operation behind it.

Three gaps matter most. The first is the valuation gap. Most private loans are marked at the lower of cost or fair value, but fair value is whatever the manager's valuation committee says it is. If a borrower is in distress but has not yet defaulted, the loan may still be marked at par, even as secondary buyers would price it at 60 to 70 cents. The token's NAV reflects the manager's view, not the market's view.

The second is the timing gap. On-chain settlement happens in seconds. Off-chain recovery takes months. During that gap, the token trades at a price that may or may not match the eventual recovery. Secondary markets for tokenized credit tokens are thin, so the printed price is often a stale oracle feed rather than a true bid.

The third is the legal-recognition gap. A token holder does not automatically own a loan. The token represents a beneficial interest in a special-purpose vehicle, Cayman fund, or Delaware LLC that holds the loans. If the wrapper entity is poorly structured, token holders may find themselves with a securities-law claim rather than a direct lender claim, which means slower access to collateral in a default and weaker standing in any bankruptcy proceeding.

Why 'tokenized' does not change recovery economics

Tokenization is a settlement and distribution layer. It is not a collateral enhancement, a credit enhancement, or a legal enhancement. Wrapping a loan in an ERC-20 token does not make the borrower more likely to pay, does not speed up foreclosure, and does not give the smart contract the power to seize a factory in another country.

This is the single most misunderstood point in the retail RWA pitch. Investors who would never buy an unrated middle-market loan directly will happily buy a tokenized pool that holds exactly such loans, because the token feels like a stablecoin. It is not. It is a securitization, with all of the structural subordination, servicer risk, and legal complexity that securitizations carry.

There are real benefits to tokenization: faster dividend distribution, more transparent reporting, easier access for non-institutional investors, and the ability to compose credit exposure with other DeFi primitives. But the loss waterfall, the recovery timeline, and the legal standing of the lender are inherited from the underlying loan documents. If the senior plus junior tranche structure is weak off-chain, the tokenized wrapper does not fix it.

Historical private credit default rates: what the data actually shows

Private credit has a shorter default history than high-yield bonds or leveraged loans, but enough data exists to ground expectations. Public BDC filings, Cliffwater Direct Lending indexes, and the annual default studies from S&P and Moody's all point to similar ranges.

Annual default rates for direct lending portfolios have averaged roughly 2 to 4 percent at the loan level over the past decade, peaking near 6 to 8 percent during the 2020 COVID shock and during the 2022 to 2023 rate-shock period. Recovery rates on first-lien senior secured loans have averaged 40 to 60 cents on the dollar, while second-lien and mezzanine recoveries have averaged 20 to 40 cents. Equity tranches in private credit funds have frequently been written down to zero in funds concentrated in a single cyclical sector.

These are averages, not guarantees. A pool heavily exposed to commercial real estate, for instance, has seen very different numbers from a pool of software recurring-revenue loans. The cyclical pattern is also important: defaults cluster, and a pool that has gone three years without a default may simply be in the calm part of the cycle.

What to read in the SPA before you deposit

The Securities Purchase Agreement, or SPA, and the related Limited Partnership Agreement or trust deed are the documents that govern your rights in a default. Most retail token holders never read them, which is exactly why issues surface only after losses occur.

Five clauses deserve attention. First, the tranche waterfall: confirm in writing what your tranche sits behind, and what losses would need to occur before you are impaired. Second, the collateral coverage test and the overcollateralization ratio: a pool that must stay 120 percent overcollateralized gives senior holders real protection; a pool with no coverage test gives them none. Third, the servicer replacement clause: who can fire the servicer, how quickly, and what triggers replacement. A poor servicer can delay recovery for years.

Fourth, the special-servicing and workout-vote provisions: when a loan goes into default, who decides whether to foreclose, restructure, or sell? If the manager controls that decision unilaterally, junior holders have limited recourse. If a majority of the affected tranche can vote, there is at least a check. Fifth, the governing law and jurisdiction clause: an English-law SPA with English courts is very different from a Cayman-law SPA with arbitration in Singapore. Local enforcement quality matters as much as the loan's collateral.

The honest yield versus risk-adjusted return comparison

Yield hunters typically compare tokenized private credit yields to stablecoin yields, ignoring the fact that the underlying risk profile is closer to a high-yield bond ETF. The honest comparison runs through a few steps.

Start with the gross yield, say 10 percent on a mezzanine tranche. Subtract expected annual loss: 3 percent default rate times 50 percent LGD equals 1.5 percent annual loss. Subtract management and servicing fees, which typically run 1 to 2 percent. Subtract an illiquidity premium, since tokenized credit tokens trade in thin markets and may not have a clean exit. The realistic net return lands somewhere between 5 and 7 percent, with a fat left tail in a bad year.

Compare that to a senior tranche paying 6 percent with near-zero expected loss, or to a high-yield bond ETF paying 7 to 8 percent with daily liquidity and published recovery assumptions. The point is not that tokenized private credit is a bad investment. For investors who understand the structure and can tolerate the illiquidity, the senior tranches in particular can be a reasonable diversifier. The point is that the headline yield is not the return. The headline yield minus realistic loss, minus fees, minus illiquidity discount, is the return.

How to track tokenized credit risk intelligently

Tokenized private credit moves slowly most of the time and then very quickly when a borrower misses a payment. Tracking which pools are exposed to which sectors, which managers are flagging loans as watchlist, and which tranches are trading at a discount to NAV is a full-time job that most retail investors cannot do alone. Zippfeed aggregates RWA headlines, flags them with sentiment scoring (bullish, neutral, or bearish) and an importance rating, so you can spot stress signals in tokenized credit before they show up in a NAV drawdown.