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Private credit has grown into a $1.7 trillion market and is projected to reach $3 trillion by 2028. Much of the discussion around this expansion has focused on familiar issues: lagged valuations, EBITDA add‑backs, covenant erosion, and the opacity of fund‑level reporting. These concerns are real, but they are not the most important blind spot.
The deeper issue is that asset owners can see the fund, but not the businesses that actually repay the loans.
The Visibility Gap: What Analysts Can and Cannot See
Fund‑level reporting provides a predictable set of metrics — internal rate of return (IRR), net asset value (NAV), deployment pace, covenant amendments, and headline default rates. These are the outputs of the portfolio.
What investors cannot see is the operating reality of the businesses that generate those outputs, including:
- Revenue and profit trends by state, sector, or revenue band;
- Whether the borrower base is strengthening or deteriorating;
- Ground‑up default probabilities by business type or geography; and
- The two‑year lag in public business‑formation data that obscures real‑time attrition.
This is not an academic gap. It is where risk accumulates quietly until it surfaces in amendments, restructurings, or valuation write-downs.
Why This Matters Now: SMB Profitability Is Under Pressure
The shift of small- and medium-sized businesses (SMBs; typically $10 million–$250 million in revenue) from bank lending to private credit is one of the most important structural changes in U.S. credit markets. These businesses now make up a large share of private-credit portfolios, yet they operate almost entirely outside public disclosure.
Across the U.S., SMB profitability has been deteriorating since 2019. Revenues have softened, but profits have fallen faster. Business formation is down in many states, while attrition is rising. Public datasets such as the BLS and Census births-deaths models lag reality by roughly two years, meaning many investors are reading a map that is already out of date. The macro environment visible in public data may not reflect the actual conditions facing the businesses inside private-credit portfolios.
SMB Profitability Trendline: 2019–2024
A Different Way to Evaluate Private Credit: Ground‑Up Benchmarking
To close this visibility gap, analysis must begin with the “borrower model,” not the fund. Once you know the types of businesses in a portfolio, their industry, revenue band, and geography, you can evaluate them against a statistically robust universe of similarly situated companies.
Structured data makes this possible. IRS-derived financial statements from millions of U.S. businesses allow analysts to construct composite income statements and balance sheets for precise peer groups. These composites show how businesses in a given segment actually perform, not the optimized version that emerges after sponsor adjustments to EBITDA.
The Benchmarking Universe and Its 3 Applications
JSI’s decade-long dataset covering 6.3 million U.S. businesses (excluding sole proprietorships), stratified by company size, industry, and state, enables three forms of borrower-level insight:
1. Income-Statement Benchmarking
Presents how businesses generate revenue, manage costs, and convert operations into profit, allowing investors to compare sponsor-reported metrics against independent financial reality.
2. Balance-Sheet Benchmarking
Shows liquidity, leverage, working-capital structure, and equity cushion, highlighting whether borrowers operate with tighter liquidity or higher leverage than their peers.
3. Forward-Looking Default-Rate Projections
Models 12‑month failure probabilities by state, sector, and revenue band, producing a granular view of risk that fund‑level default rates cannot capture.
Case Study: Manufacturing Companies, $10M–$50M Revenue
Consider a portfolio concentrated in manufacturing companies with $10 million–$50 million in revenue. Benchmarking against the peer group shows a segment operating with thin margins and limited cushion in a high-rate environment.
The median business generates roughly $28 million in revenue, with EBITDA margins around 11% and net income margins of 6%–7%, down from 9% in 2020. Sponsor-reported EBITDA often inflates these figures by 25%–40% through add-backs, meaning fund-level numbers may overstate borrower strength.
Income Statement
On the balance sheet, the typical business carries 42% debt-to-assets, holds 12%–13% of assets in cash, and operates with a DSCR of 1.21x, leaving little room for error as interest costs rise.
Balance Sheet
For advisors, the question becomes: If this is what the typical business looks like, how do the borrowers in the portfolio compare?
Default Risk Varies Dramatically by State, Sector, and Size
Borrower-level analysis also reveals how uneven performance is across the country. When failure-rate projections are modeled by company size, industry, and state, a far more granular picture emerges.
For example, 12-month default probabilities vary as seen below:
- 2.9% for Illinois healthcare services
- 4%–5% for Midwestern manufacturing
- 6.1% for California consumer retail
A single fund-level default rate can mask pockets of concentrated risk running three to five times higher.
Commercial Default Rates Example: 12-Month Projections
What This Means for Asset Owners
Fund-level analytics can be strengthened, but they cannot stand alone. They tell you how a portfolio is performing today, not whether the businesses inside it are strengthening, weakening, or quietly accumulating risk.
A borrower-level, ground-up approach provides:
- A clearer picture of the economic reality facing SMB borrowers;
- A way to benchmark sponsor-reported metrics against independent data;
- A framework for identifying concentrations of risk that fund-level reporting obscures; and
- A more informed basis for manager selection and portfolio construction.
In a market where opacity is the norm, visibility becomes a competitive advantage.
To address this analytical blind spot, JSI developed a benchmarking dataset built from the income-statement and balance-sheet filings of 6.3 million U.S. businesses (excluding sole proprietorships). Spanning 2014 through 2024 and stratified by company size, industry, and state, the dataset enables analysts to compare any borrower segment to a statistically robust peer group.
With such a bottom-up view, asset owners can evaluate private-credit portfolios using the actual financial contours of the businesses that resemble those inside the portfolio, something fund-level metrics alone cannot provide.
Paul Hill is a business economist, entrepreneur, inventor/holder of multiple patents, and developer of economic forecasting and risk management tools. He is a former lecturer and advisor for the UCLA Anderson School, as well as an advisory board member for the Computer Science Department at Santa Monica College. As the founder and president of JSI Analytics in 2008, and co-founder of EducateToCareer.org, he and his team provided actuarial services to lenders and servicers of over $200 billion in student loans. JSI Analytics currently provides a suite of tools for economic trends analysis and credit risk management, which are utilized by fixed income investors, commercial banking, and trade credit industries.
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