The Real Challenges and Risks of Using AI in Private Credit

AI is showing up everywhere these days, including private credit. Some funds use it to help with loan underwriting, portfolio monitoring, or borrower reporting. The idea is pretty simple: get faster insights, make smarter decisions, and maybe cut down on manual work.

But for all the excitement, using AI for private credit comes with its own set of real-world challenges and risks. Things like data quality, transparency, regulation, and plain old human oversight don’t just disappear because you’ve got a fancy algorithm running in the background.

Let’s talk through where those risks show up—and why they matter if you care about fund performance, investor trust, and staying out of trouble with regulators.

First Up: AI Is Only as Good as Your Data

Everyone says this because it’s true. If you feed messy or incomplete data into an AI model, it’s going to make bad decisions.

In private credit, this can get complicated fast. Borrower financials don’t always come in cleanly. Asset-based lending depends on up-to-date collateral reports, borrowing base data, and borrower compliance certificates. If those inputs aren’t accurate or timely, any AI-driven insights or decisions could lead a fund in the wrong direction.

For example:

• A model predicting borrower default risk needs clean payment history and financial ratios.
• AI tools analyzing Private Credit Valuations rely on consistent valuation marks and portfolio data.

It’s not just about volume of data—it’s about quality and structure. That’s why Fund Finance Technology platforms matter here. They help organize and clean the data before it ever touches an AI model.

Transparency: Can You Actually Explain What the AI Is Doing?

AI models, especially more complex ones like machine learning systems, aren’t always easy to unpack. You can end up with what people call a black box problem: the model spits out an answer, but no one really understands why.

In private credit, that’s risky.

Let’s say an AI tool recommends lowering the value of a loan or tightening borrower terms. You’re going to have investors, regulators, maybe even borrowers asking, “Why?”

If the answer is, “That’s just what the model says,” that’s not going to cut it.

For funds using AI in areas like Borrowing Base Management, Lender Compliance Technology, or ESMA Reporting, transparency isn’t optional. Regulators expect clear, defensible explanations for any decisions that impact valuations, reporting, or fund governance.

This is where simpler models or rule-based systems sometimes make more sense than full-blown machine learning. They’re easier to audit, even if they aren’t quite as powerful.

Regulatory Concerns Aren’t Theoretical

AI in Private Credit isn’t happening in a vacuum. Regulators are paying attention. In Europe, ESMA (European Securities and Markets Authority) has already flagged concerns about AI use in finance. The same goes for the SEC in the US.

The main issues?

• Fair treatment of borrowers
• Accurate investor reporting
• Model governance and auditability

If an AI tool recommends actions that unintentionally discriminate against certain types of borrowers, that’s a problem. If AI-driven valuation marks lead to misleading NAV or IRR figures, that’s another problem.

This is especially tricky for funds involved in Asset-based Lending, where AI might influence collateral tracking or loan eligibility. If the system flags too aggressively—or not aggressively enough—you could end up out of step with both internal policies and external rules.

Bottom line: any AI system used in private credit needs to be designed with regulatory expectations in mind from the start, not bolted on after something goes wrong.

Model Governance: Someone Has to Own It

AI doesn’t manage itself.

One risk that doesn’t get talked about enough is model drift. That’s when an AI model slowly gets less accurate because market conditions, borrower behavior, or input data patterns change over time.

In private credit, this could quietly erode fund performance if no one’s watching.

That’s why model governance matters. Every AI-driven tool needs:

• A clear owner or team responsible for monitoring performance
• Regular reviews and recalibrations
• A process for shutting down or rolling back the model if things start to go off course

Private credit software that includes AI features usually offers some model monitoring tools, but someone still has to pay attention and step in when needed. You can’t set it and forget it.

The People Factor: Human Judgment Still Counts

One of the biggest risks with AI is assuming it can replace human judgment completely. It can’t.

Even the best AI in private credit should be treated as a decision support tool, not a decision maker.

For example:

• If AI flags a borrower as high risk, a portfolio manager should review that manually before taking action.
• If AI suggests adjusting Private Credit Valuations, there needs to be a valuation committee or team review.

Good private credit management already depends on balancing data, market insight, and borrower relationships. AI can help surface things faster or highlight patterns people might miss—but it doesn’t replace the conversation.

Integration Challenges: Getting AI to Play Nice with Existing Systems

Most private credit funds aren’t building everything from scratch. They’ve already got Fund Finance Technology platforms, Borrowing Base Management systems, and Lender Compliance Technology running in the background.

Getting AI tools to work alongside those setups isn’t always smooth.

• Data formats might not match up.
• Reporting outputs might not fit into established investor reporting workflows.
• Security and privacy controls need to align.

These things slow down adoption, but they’re necessary checks. Rushing AI into a private credit workflow without thinking about integration usually creates more problems than it solves.

Real-World Example: AI in Borrowing Base Monitoring

Say you’re managing a fund that does a lot of asset-based lending. AI could help by automatically reviewing borrower collateral reports and flagging discrepancies. That sounds great on paper.

But:

• If borrower data is late or incorrect, AI can’t magically fix it.
• If the model overreacts to a small data blip, borrowers could get penalized unfairly.
• If regulators ask for an explanation, someone needs to be able to show exactly how decisions were made.

This isn’t a reason not to use AI—it’s just a reason to use it carefully, with the right checks in place.

Wrapping It Up: No Magic, Just Tools

AI in Private Credit isn’t a magic button that solves everything. It’s a tool. Like any tool, it works best when people understand its limits and pay attention while using it.

The biggest risks aren’t the technology itself—it’s forgetting to double-check the outputs, relying too much on black-box models, or skipping over governance steps.

If your fund is thinking about rolling out AI, start with small, clear use cases:

• Portfolio monitoring alerts
• Borrower compliance checks
• Data quality reviews

Make sure human review stays in the loop, set clear ownership for the models, and keep regulators in mind.

That’s how you avoid the pitfalls—and make sure AI is actually helping your fund perform better, not just making things more complicated.

Frequently Asked Questions About AI in Private Credit

1. Does using AI in private credit mean fund managers get replaced?

Not really. AI helps with tasks like flagging borrower risks or automating compliance checks, but human judgment is still essential. Managers still make the final calls on lending decisions, portfolio management, and investor reporting. AI’s more of a tool than a replacement.

2. What’s the biggest risk with AI in private credit?

The biggest risk is trusting the model too much without oversight. If the AI is working off bad data or its assumptions go stale, it can quietly lead a fund in the wrong direction. That’s why model governance—checking the system regularly and adjusting when needed—is so important.

3. How does AI fit into asset-based lending specifically?

In asset-based lending, AI can help monitor borrower collateral reports, spot anomalies in borrowing base calculations, and streamline compliance checks. But again, AI needs clean, up-to-date data to work well. It can’t replace having actual conversations with borrowers when things change.

4. Can AI handle private credit valuations?

AI can help flag potential valuation issues or run scenarios, but most funds still rely on human valuation committees to review and approve final marks. Investors and regulators expect transparency and consistency, which means people still need to be involved.

5. Is AI use in private credit regulated?

Yes, indirectly. Regulators like ESMA and the SEC pay attention to how funds handle valuations, risk management, and investor reporting. If AI is part of those processes, funds need to make sure they can explain and audit what the system is doing. You can’t just say, “The model told us to.”

6. What kind of private credit software supports AI use?

Fund finance technology platforms are starting to include AI features for things like Borrowing Base Management, lender compliance technology, and portfolio monitoring. The key is choosing tools that offer transparency, audit trails, and easy integration with your existing systems.