Private credit is now one of the biggest pools of capital in finance. By the end of this decade, it’s predicted to hit $5 trillion, with the corporate segment alone expected to roughly double over the next five years. And, driven by a pursuit of high yields, flexible terms, and quicker execution, the number of players in the industry continues to grow.
However, that which makes private credit attractive also makes its risk hard to see. Credit Benchmark has a unique view on the market – including a large number of private entities – as a result of data we collect from over 40 contributing banks, we’ve identified three big challenges:
- Since the loans don’t trade, and most borrowers have no rating, it’s hard to assess a single borrower or see how exposures cluster across a portfolio. It’s even harder since some private credit lending is sponsor-backed (the borrower is a company owned by a private equity firm, which negotiates the loan and controls the business).
- With no external rating or market spread to triangulate against, you can’t confirm that your PD is calibrated correctly, prove that your loan is priced for its true default risk, or defend the rating when an examiner tests it.
- Because the loans are illiquid and the private credit information is available only to a limited set of parties involved, deterioration is hard to catch early across a large book, and fund investors can ask for money back faster than the underlying loans can be exited.
These challenges, though significant, are not meant to deter participation in the private credit market. Rather, they drive home the importance of proactive credit risk management. Because institutions that spot deterioration earlier, price it more accurately, and defend their calls under scrutiny gain an advantage over others in the market.
This article walks through six challenges, categorized into three pillars, that stand between risk teams and clarity, and the practical ways teams are closing each.
Pillar 1: Visibility Into Current Risk
1. The coverage gap
According to the IMF, private credit loans are unrated, rarely traded, typically marked to model by third-party pricing services, and without standardized contract terms. Similarly, in a 2026 survey of senior risk and investment professionals, more than 70% identified visibility into private borrowers as a significant pain point, and 72% specifically cited obtaining up-to-date data on private entities as a challenge. Due to this data gap, risks may be difficult to detect in advance. Plus, pricing risk and comprehensively assessing entities is significantly harder.
This coverage gap, when left open, has practical implications. A PD you cannot benchmark feeds into an IFRS 9 or CECL provision that you then have to defend. A risk grade assigned without an external reference flows into IRB capital and into what you tell an examiner.
2. The concentration gap
Concentration risk is familiar to every credit risk professional, who is taught to manage it with limits and diversification. However, private credit makes limiting the concentration risk harder for two key reasons:
First, the asset class clusters. The IMF’s own analysis describes the typical private credit borrower as a mid-sized company carrying high leverage, with the technology sector overrepresented. And the EDGAR-mapped holdings of large funds skew toward a familiar set of sectors: industrials, financials, healthcare, consumer, and technology. So, a portfolio that seems diversified on the surface may actually be concentrated.
Second, you cannot track portfolio clusters as you would in public markets. Since private names have no traded price, the market-derived correlation you would use to see how your exposures move together is unavailable for a significant portion of the credit book.
The exposure is also easy to understate, as fund and SPV wrappers obscure the true exposure and hide overlap with a single sponsor or end market. A portfolio can, therefore, be more correlated than reporting shows.
Pillar 2: Integrity: Trusting and Defending Read Of The Risk
3. The pricing gap
Questions from credit committees boil down to this : does the spread compensate for the borrower’s actual default probability, and the recovery you would realistically achieve if it failed? In public markets, you can sanity-check your answer against several independent signals. But since that’s not possible in private credit, teams use internal models or sponsor-supplied financials as both the input and the check. This approach produces a pricing view formed inside a closed loop, with nothing external to test it against, which, as recent examples show, can be costly.
First Brands Group, an aftermarket auto parts roll-up, filed for Chapter 11 after roughly $12 billion in liabilities surfaced that had not been visible to its lenders, including significant off-balance-sheet supply-chain financing. Underwriters who believed they were lending to a borrower with around 5x leverage discovered the real figure was closer to 20x. However, the commonality of such practice across the private credit industry is unknown.
4. The validation & defensibility gap
When borrowers are unrated, entities rely solely on their internal models. That places the entire weight of model risk on a single internal view, creating two major problems.
The first is calibration. Validating a PD model normally relies on observed defaults. However, because private credit portfolios are inherently low-default and often young, their internal loss history is too thin to validate the model. This causes the model to fall short of SR 26-2’s benchmarking guidance.
The second problem is defensibility. Beyond being internally consistent, regulators expect a model to have independent validation. But for private entities, external benchmarks rarely exist. As such, the model becomes indefensible, causing regulators to impose capital add-ons or require the bank to fall back on standardized approaches. This eliminates any capital advantage the internal model would have provided.
Pillar 3: Operational resilience: acting in time and managing the structural mismatch
5. The speed gap
Half of the risk teams surveyed by Credit Benchmark actively monitor 250 or more obligors, often far more. At that book size, spreadsheets and disconnected platforms stop being tools and become sources of operational risk in their own right. A separate SS&Ctech industry survey found more than half of teams cite manual data entry and fragmented systems as major constraints on their effectiveness.
Further complicating prompt responses to risk exposures is that warning signals typically lag. Covenants only trigger upon breach, and rating agencies update ratings quarterly. By the time either signal is available, deterioration has set in, and the opportunity to use loss-mitigation strategies (covenant amendment, limit reduction, exit at par) has closed. What’s left are options that are too expensive.
6. The liquidity and redemption gap
Semi-liquid and evergreen fund vehicles, including non-traded business development companies (BDCs) and interval funds, brought a wider investor base into private credit by offering periodic redemptions, typically quarterly and capped at around 5% of net asset value. The underlying loans, however, are long-dated and genuinely illiquid.
When redemption requests spike, the mismatch between the fund’s promised redemption cadence and the liquidity of the underlying assets becomes apparent. That happened at the end of 2025, when redemption rates across non-listed BDCs nearly tripled in a single quarter, rising from 1.6% of Net Asset Value (NAV) in Q3 to 4.5% in Q4, well above the typical 2-3% baseline. This severely tested funds, resulting in:
- Blue Owl saw redemption requests hit 40.7% of shares in its technology-focused vehicles, permanently closed the gates on its $1.6bn OBDC II fund, and sold approximately $1.4bn of loans to meet remaining demand.
- BlackRock TCP Capital recorded a 19% NAV write-down in Q4 2025. Meanwhile, Blackstone lifted the quarterly redemption cap on its flagship private credit fund from 5% to 7.9% to meet investor demand.
- Outside BDCs, roughly C$30 billion of Canadian private real estate funds (about 40% of that sector) were gated as managers halted redemptions or extended withdrawal timelines.
These six gaps share one root cause, a private market that has outgrown the tools built to see into it, and a common set of responses. The following section lays out how leading risk teams tackle them.
Credit Risk Management Best Practices Teams Use To Tackle These Challenges
Teams we speak to find ways to overcome these challenges, maximizing the upside while minimizing their exposure. Here’s an overview of the best practices they use and the challenges each one solves:
1. Establish a risk framework with clear ownership
The foundational practice in private credit risk management is creating a documented risk framework with explicit ownership. That way, decisions are consistent across teams, escalation is predictable, and accountability is clear.
The three-lines-of-defence model is a long-established risk governance standard used across banks and other regulated financial institutions, which encompasses:
- Origination and portfolio management. This team makes the credit calls and bears the P&L consequences when they are wrong.
- Independent risk and compliance. This team writes the credit policies that the first line must follow, sets the limits within which it can lend, and reviews individual deals against those rules. It should not report into the first line.
- Internal auditing, which periodically checks that the first two lines are operating according to the framework.
For large exposures, and particularly for the largest counterparties, naming a specific individual as accountable rather than a team should be best practice. Team-level ownership creates vague reporting lines.
The entire framework should be grounded in a clear statement on the company’s risk appetite. This should also include concrete limits, what happens when limits are breached, and a mandatory inclusion of private credit exposures as a standing agenda item in credit and risk committee meetings.
2. Run disciplined due diligence and stay conservative when information is thin
For private borrowers, the only meaningful source of information is the borrower itself. That makes due diligence heavier than in public markets, requiring four specific tests:
- Read the whole capital structure, not just the disclosed balance sheet. Off-balance-sheet financing, inter-company loans, and supplier facilities are the items that most often surface unexpectedly in distress.
- Test the sponsor across cycles. A track record built entirely in the 2020–2024 environment tells you less than one that includes a downturn.
- Model the borrower’s cash flows against your own downside scenarios, not the projections management provides. Management projections systematically understate the downside.
- Verify what you can against independent sources, such as rated debt elsewhere in the capital structure, comparable public peers, trade payment data, and sponsor references from prior transactions.
It’s not every day the borrower supplies all the information when requested. In such cases, defer to Basel’s guidance, which recommends applying a more conservative rating, tighter limits, or pausing the relationship when a borrower fails to provide adequate information. If the entity provides all the requested information, put it in writing and enforce it religiously.
3. Build an independent second read on every unrated borrower
Unlike public borrowers, private entities mostly lack external benchmarks to validate PDs produced via internal models. Still, external validation is a key component of credit risk management. So, to fill the gap, credit teams build a second opinion themselves or find a reliable third-party opinion.
To build a second opinion, you can either:
- Standardize the internal assessment. Bring unrated names onto a single internal scorecard so every borrower is judged against the same framework. That way, the read is comparable throughout the book, and the team applies the same standards to every deal.
- Triangulate from indirect signals. Although no public reference exists on the borrower itself, there is almost always something adjacent that does. This could be a track record across previous vintages that predicts underwriting quality, rated peers in the same sector, supply chain and trade payment data, rated debt elsewhere in the borrower’s capital structure, and so on.
An independent assessment of the private entity (where it exists) can be layered upon what you’ve already built. This could be private ratings from specialized agencies, some of which now cover a larger share of private credit than they did five years ago. You should, however, be mindful that supervisors, including the IMF and the NAIC, have flagged concerns about their calibration relative to independent assessment. A stronger and more reliable alternative is consensus-based credit assessments, which aggregate the internal credit views of multiple lending institutions for the same borrower.
4. Set concentration limits on look-through exposure
The problem with private credit is that the true shape of an exposure is often obscured by the vehicles that hold it. Fund and SPV wrappers hide who the underlying borrower is and which sponsor stands behind it. A thorough look-through addresses this.
Instead of treating each fund or SPV as a single line of exposure, look through the vehicle to the underlying borrowers and count your exposure to each. Then add up all your exposure to the same borrower, wherever it appears and apply your concentration limits to that total, not to the wrapper.
Limits also need more dimensions than single-name and broad sector buckets. The overlaps that matter often sit across categories that a standard framework treats as separate:
- By the ultimate sponsor. The same private equity sponsor may appear behind borrowers in three different SIC codes, through three different vehicles.
- By end-market. A borrower selling into consumer discretionary spending faces the same demand shock as one selling into consumer durables, whatever their formal industry classifications.
- By shared risk factor. Rate sensitivity, leverage level, or exposure to a specific supply chain can cluster borrowers that a sector taxonomy would show as diversified.
Correlation analysis across the whole book is what surfaces these clusters. Without it, the reports will show a diversified portfolio that isn’t actually diversified.
5. Stress-test modularly, correlation-aware, and let it feed decisions
Strong stress testing has three properties:
- It is modular. The test is built in pieces, one for each type of exposure, then rolled up to see the picture at the sponsor level and across the whole book.
- It is correlation-aware. It explicitly assumes that default and loss correlations rise under stress, rather than holding correlations constant at benign-market levels.
- Its results feed real decisions on limits, pricing, and close-out planning, rather than sitting in a document.
The first two properties matter because siloed, correlation-blind stress tests systematically understate portfolio risk. The third bullet point is just as important as the recent Archegos failure shows. In the Basel Committee’s account, one of the banks with material exposure to Archegos realized a collateral shortfall of more than $1 billion at default, against no projected shortfall under the standard PFE metric. That’s because its stress-testing framework wasn’t producing numbers the trading desk could act on when sizing positions, setting margin, or predicting the fallout of the exposure. There’s no use in a stress test that doesn’t feed into limits, pricing, or close-out decisions.
6. Enforce spread-to-PD discipline
Before underwriting, and at every periodic review, regularly confirm that the spread on each loan compensates for the borrower’s default probability and the recovery you’d realistically get if it defaulted.
Once you have the number, test it two ways. First, against similar deals in your own book to check the deal is priced consistently, and against an external benchmark. Then document what you did, so the next reviewer isn’t reconstructing it from the deal file six months later.
If pricing has drifted from PD without a documented reason, that is an early sign of risk-reward misalignment. Catching it early matters because the drift compounds. One deal priced tighter than the model called for, then another, then the sector’s pricing convention shifts, and a year later, the book is systematically underpriced for its risk without anyone having explicitly signed off on that.
7. Benchmark internal models against independent external references
For most private credit borrowers, the internal model is the only rating the name has. That is fine for setting a grade, but it is a problem for validating one.
A model built on low-default, unrated portfolios cannot be checked against its own internal loss history as there simply aren’t enough defaults to draw a statistical conclusion. An external source has to be used to validate it. Even regulatory guidance drives home the importance of benchmarking against independent external references across three core components: conceptual soundness, ongoing monitoring, including benchmarking, and outcomes analysis.
One solution is to construct benchmarks from whatever independent references the asset class does provide. This includes:
- Any private ratings on the borrower or its capital structure.
- Market-implied credit measures, where they exist on comparable public names.
- Peer data from insurers and asset managers publishing sector-level default and recovery statistics.
- Consensus-based credit assessments that aggregate the internal credit views of multiple lending institutions on the same borrower.
The point is to have several validation sources and always document what you benchmarked against, how the internal model performed, and what you changed as a result. This documentation will come in handy when an examiner asks about your methodology for validating low-default portfolio.
8. Use dynamic ratings and trigger-based monitoring, not scheduled reviews alone
Relying on annual or quarterly reviews isn’t ideal, as a borrower’s risk profile could deteriorate before the next review cycle. It’s best practice to base review frequencies on each borrower’s inherent risk and exposure volatility. A stable, low-leverage name might genuinely be fine on an annual cycle, while a highly leveraged one at the edge of a covenant needs continuous reviews.
Regardless of the bucket it falls into, any significant change to the risk profile, such as a rating action on a similar entity, should trigger an immediate, fresh assessment, even if a review was recently conducted. This is in line with basel’s guidance, which makes it clear that covenants and close-out rights are not substitutes for active monitoring.
Learn how financial institutions use Credit Benchmark for continuous monitoring
9. Maintain a watchlist and rehearse default management
A watchlist enables credit teams to act on warning signals identified during monitoring. Without one, a name can be visibly deteriorating in the data for months while no one with the authority to intervene actually does.
A strong watchlist has three parts.
- Graded tiers, not a binary list. Borrowers should be assigned to tiers that reflect the urgency of the situation. For example: Tier 1 for names that need closer monitoring; Tier 2 for names heading toward restructuring; Tier 3 for names likely to default. Each tier reflects a different level of concern and calls for a different response.
- Pre-defined actions for each tier. When a borrower moves into a tier, the adequate response should already be scoped. Tier 1 might mean monthly reviews and reduced credit limits. Tier 2 might mean bringing in a workout team and drafting a restructuring plan. Tier 3 might mean identifying an exit strategy and pre-positioning for a sale. That way, you act quickly rather than debating next steps in a committee meeting while deterioration is already accelerating.
- One person accountable for each name. Someone on the team should be responsible for taking action on each borrower.
Alongside a watchlist are sound default management guidelines that outline the response to defaults. Who gets contacted, in what order? What is the close-out protocol? Are your intercreditor arrangements already negotiated? Do you know who will buy the assets if you need to sell? Each of these needs an answer before the default happens. That’s why the ECB recommends that entities regularly run fire drills to test close-out and default procedures.
10. Match fund liquidity terms to asset liquidity and pre-build the tools
The way to minimize risk in a semi-liquid fund is to resolve the mismatch between what investors were promised and what the assets can deliver. And the first way to do that is to match the promises to the assets. It’s illogical to promise monthly or quarterly redemptions when a fund’s loans have a five-year weighted-average life and can’t be sold quickly at their marked value.
Additionally, liquidity tools for controlling the pace of redemptions when demand for cash outstrips the fund’s ability to raise it should be pre-built from the fund’s inception. This includes:
- Redemption caps: Limits on how much can be redeemed in a given period (typically 5% of NAV per quarter).
- Gates: Mechanisms that halt or slow redemptions when requests exceed a threshold.
- Notice periods: Time delays between request and payout, giving the fund room to liquidate in an orderly way.
- Side pockets: Separate structures that isolate illiquid or troubled assets, allowing redemptions from the healthy portion to continue.
- Swing pricing: NAV adjustments that make redeeming investors bear some of the transaction cost.
Define and disclose these before investors subscribe, and calibrate them against realistic stress. Test redemptions against genuinely liquid assets and model the cost of winding down illiquid positions in a forced sale.
How Credit Benchmark Enables Effective Private Risk Management With Enhanced Visibility
Of the six challenges, four trace back to the same missing input: an independent, forward-looking read on borrowers whose internal models you cannot validate, whose ratings either do not exist or come from sources whose accuracy supervisors have flagged, and whose traded price you do not have. That missing input is what Credit Benchmark provides.
We aggregate the internal credit risk views that more than 40 global banks already produce for their own borrowers, anonymize and standardize them, and publish the resulting consensus as a Credit Consensus Rating and a forward-looking PD for each entity. Coverage extends to more than 120,000 legal entities, mostly unrated funds, private companies, and financial counterparties.
Credit Benchmark data updates weekly. So, fundamental shifts in credit conditions are captured far before deterioration sets in. This makes it a dependable external model-validation tool alongside internal models, market prices, and agency ratings, and a valuable tool for monitoring model drift.
To limit concentration risk, Credit Benchmark’s consensus PDs enable correlation analysis across exposures, including those for non-trading entities. The output supports look-through concentration measurement and cross-sector correlation.
Two examples from our own client base illustrate the value of having Credit Benchmark in a risk management infrastructure.
[Case Study Placeholder]: 1 anonymous one on an asset manager
If you would like to see how this works on names you actually hold, we offer an assessment of which of your borrowers have a Credit Consensus Rating in the database, the consensus view on each, and a fund-specific version of the risk-return plot from our published research.
Book a demo to request a coverage assessment for your counterparty portfolio.