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Private-credit funds manage AI-related borrower risk by examining how a company could be affected by AI, testing whether it can still generate enough cash to repay its loan, and monitoring protections, maturities, and portfolio concentrations. “AI-dependent” is not a standardized borrower category: a company may be exposed to AI competition, benefit from using AI, rely on AI infrastructure, or face several of these at once.
How AI can become a credit risk
AI affects a loan when it changes the borrower’s ability to generate cash, meet interest and principal payments, or refinance at maturity. A software company might lose customers if a competing AI product replaces part of its offering, face pricing pressure if customers can build alternatives, or spend more to keep its product competitive. Conversely, AI may improve its product or reduce its operating costs. The relevant question for a lender is not whether a company is “in AI,” but how these changes could affect its specific business and debt.
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J.P. Morgan Asset Management identifies four channels to examine: revenue erosion, margin compression, valuation compression, and a refinancing seizure. The first two can weaken repayment capacity directly. Valuation declines can reduce the value of a business or its collateral, while a loss of refinancing access matters especially when a loan is approaching maturity. These risks can interact, but they do not necessarily arrive at the same time.
What managers examine in an individual loan
A useful assessment connects the borrower’s competitive position to its capacity to service debt. The following is an analytical checklist, not a universal regulator-mandated scorecard.
Business model and ability to adapt
- What specific task does the product perform, and how costly or difficult would it be for customers to switch, build an alternative, or reduce their use?
- Could AI strengthen the product, make it easier to replicate, or do both? Does the company depend on third-party models, cloud services, or other technology providers?
- How are customers charged, and could AI change pricing power or the amount customers are willing to pay?
- How durable are demand, renewals, and customer relationships if competitors introduce AI-enabled alternatives?
Cash flow, leverage, and lender protection
- Recurring revenue, retention, customer concentration, gross margins, operating costs, and cash generation help indicate whether the company can withstand changes in demand or pricing.
- Leverage, interest coverage, covenant headroom, liquidity, and debt maturity show how much room the borrower has to absorb weaker results or higher financing pressure.
- Collateral value, loan seniority, sponsor support, and access to refinancing affect potential recovery or the borrower’s ability to address a shortfall.
The Federal Reserve has highlighted how high leverage and floating-rate borrowing can leave borrowers more vulnerable to shocks. These indicators should be read together: stable current payments do not, on their own, establish that a borrower can refinance on acceptable terms later.
Timing relative to maturity
Managers need to compare the time a business might need to adapt with the time remaining before its debt comes due. A borrower can remain current while its business valuation or refinancing prospects worsen. In a March 31, 2026 shareholder update, Oaktree Strategic Credit Fund said pressure in its portfolio had been concentrated in older, pre-2022 vintages and ARR loans facing 2027–2028 maturities. The update described the fund’s use of operating KPIs, financial metrics, and AI-related considerations in a business-resilience framework. That is one manager’s reported practice, not evidence that all private-credit funds use the same framework.
What managers monitor after making a loan
Underwriting is a point-in-time assessment; borrower conditions can change. Ongoing monitoring can track operating performance, covenant tests, liquidity, payment behavior, waivers, valuation changes, and developments that may affect refinancing. Reporting requirements and covenant terms determine, in part, what information lenders receive and when they can respond to weakening performance.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFunds can also aggregate exposures across their portfolios. Relevant groupings include sector, product type, sponsor, loan vintage, maturity, shared borrowers, and reliance on common technology or financing providers. The Bank for International Settlements (BIS) found that some large business development companies (BDCs) had exposure to a shared pool of borrowers. The Financial Stability Board (FSB) has warned that technology-sector concentration, interconnected financing, valuation opacity, and limited loan-level data make system-wide exposure difficult to assess.
How loan protections help—and what they cannot do
Seniority, collateral, covenants, reporting obligations, and limits on additional debt can affect a lender’s recovery or ability to respond when performance weakens. They do not make a borrower’s product immune to AI-driven competition or guarantee repayment. The Federal Reserve cautions that competition and pressure to deploy capital can weaken underwriting standards or lead to more covenant-lite lending. The protections that matter depend on the loan’s terms and the risks identified in the borrower’s business.
How to compare AI-related exposure across loans or funds
A headline exposure figure alone can obscure important differences. Compare loans along the following dimensions; the appropriate weight for each depends on the loan and the fund’s mandate.
| Dimension | What to assess | Why it matters to credit |
|---|---|---|
| AI disruption and adaptability | Whether AI could replace or strengthen the product, and how readily the borrower can respond | Helps frame possible changes to demand, pricing, and competitive position |
| Revenue resilience | Recurring revenue, customer retention, concentration, and pricing power | Indicates how changes in customer behavior could affect cash generation |
| Margins and cash flow | Exposure of gross margins, operating costs, and cash generation to disruption or investment needs | Connects business changes to capacity to pay interest and principal |
| Debt capacity | Leverage, interest coverage, liquidity, and covenant headroom | Shows the borrower’s financial room to absorb a shock |
| Lender position | Seniority, collateral, covenant terms, and reporting requirements | Affects potential recovery and the lender’s ability to respond |
| Timing | Debt maturity compared with plausible adaptation and refinancing timelines | Highlights whether pressure could become acute before the borrower can adjust |
| Concentration and dependencies | Related borrowers, sponsors, vintages, maturities, and shared technology or financing providers | Can reveal correlated exposure that is not obvious from one loan viewed alone |
What public exposure figures do—and do not—show
Published figures indicate that software and SaaS are material areas of private-credit exposure, but they describe different borrower groups and market measures. They are not a measure of the share of borrowers whose repayment capacity has already worsened because of AI.
| Reported measure | Scope and interpretation |
|---|---|
| About $115 billion | BIS reported this as BDC lending to software firms in 2026: about one fifth of BDC lending and more than 80% of BDC technology portfolios. |
| More than $500 billion | BIS reported outstanding private-credit loans to SaaS firms at the end of 2025, equal to 19% of total direct loans; it also reported that one third of private-credit funds had extended loans to the SaaS sector. This is a broader private-credit measure than the BDC figure above, so the amounts should not be treated as directly comparable. |
| Almost 30%; about 10%; around 5 percentage points | BIS reported that software-company stock prices declined almost 30% between October 2025 and February 2026. Over that period, BDC stocks fell about 10% on average, and BDCs with high software exposure underperformed those with low exposure by around 5 percentage points. These are market-price movements, not private-loan default rates. |
| 53%; 54%; 16% | PwC’s 2026 survey page reported that 53% of respondents were more frequently implementing technology in private-credit investment processes, 54% were most likely to use AI in underwriting, and 16% viewed AI-enabled portfolio management as a current priority. These are survey responses, not market-wide adoption rates. |
| Around $220 billion | The FSB reported drawn and undrawn bank credit lines to private-credit funds captured in available data across FSB members. Some commercial estimates ranged from $270 billion to $500 billion. These figures indicate interconnection between banks and funds; they do not measure AI-specific borrower exposure. |
In July 2026, BIS reported that AI-related revenue uncertainty had not yet affected the BDC software loans it studied or changed how BDCs and their equity investors priced those exposures. Alongside that finding, BIS documented software-market repricing and weaker share-price performance among BDCs with higher SaaS exposure. The price movements are a reason to monitor risk, not evidence that AI has caused sector-wide private-credit defaults.
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Where AI tools fit in the credit process
AI tools can assist with work that has checkable inputs and outputs, such as extracting terms from credit agreements, summarizing data rooms, comparing covenant definitions, flagging reporting exceptions, and organizing portfolio monitoring. They can help staff find items to review; they do not replace the judgment needed to assess a borrower’s business model, repayment capacity, or likely recovery.
PwC’s 2026 survey page emphasizes data quality, integrated workflows, and governance, and says final economic judgment remains human. PwC US partner Erich Butters described the direction this way: “In our view, the next stage of evolution will be the combination of better data, better monitoring, and more active asset management underpinned by agentic AI to increase efficiency and control across the end-to-end process.”
A CRISIL vendor-authored case study describes a US fund using an LLM-based tool to review loan agreements and covenant data across about 100 active deals, identify exceptions, and support borrower engagement. It is an example of a possible workflow, not independent evidence that such tools deliver the same results across funds.
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Private-credit loan data are less transparent than public-market data. The FSB identifies limited fund- and loan-level information, inconsistent definitions, valuation opacity, and concentration as obstacles to assessing exposures and transmission channels. Public manager examples describe those managers’ own portfolios and methods, so they should not be generalized to every fund.
The cited sources do not establish what share of all private-credit borrowers has already experienced weaker repayment capacity specifically because of AI, and they do not validate a single industry-wide AI-disruption scorecard. Exposure estimates, share-price declines, and a vendor case study each illuminate a different part of the issue; none by itself establishes AI-caused loan impairment.
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