Summary
Private loan contracts define rules that map naturally to automation once terms are validated and in production. Amendments and credit monitoring introduce interpretive judgment that automation cannot replace. The firms scaling most effectively are the ones making that difference explicit in their operating model.
From an operational perspective, a private loan contract is a set of rules. It includes data like accrual schedules, repayment structures, drawdown triggers, and covenant thresholds tailored to each fund and borrower. Operations teams extract loan terms from source documents, validate them, and then load them into production as the authoritative data for loan servicing in a portfolio. Achieving that efficiently while working within the realities of a highly customizable market was a central theme at the LSTA 2026 Operations and Technology Conference.i
Once validated terms are in production, the operations team has the data they need to manage the loan. What follows becomes deterministic processing.
The complication is amendments. As terms get updated or replaced, the rules change. Simple changes, such as a rate adjustment or a revised reporting date, translate into operational data, so they don’t materially affect processing or automation. But others require someone to determine what the amended terms mean for the loan’s risk profile or accounting treatment. Something like a restructured collateral definition changes your position more substantially. That’s a different design problem for anyone looking to improve efficiency with automation.
Some types of automation can help start the process by turning loan agreements into structured data. Optical Character Recognition (OCR) and machine learning (ML) tools can pull interest rates, payment dates, waterfall priorities, and covenant thresholds out of a credit agreement in a fraction of the time it would otherwise take. AI is improving both the speed and accuracy of that extraction.
The question is where that extraction reliably produces operational data and where it surfaces something that still needs a human decision.
When the document-to-operational flow follows the same pattern across loans, you can apply it repeatedly. OCR and ML tools learn from familiarity, but private credit portfolios tend to run on unfamiliarity. New facilities often introduce unfamiliar structures while amendments change the rules.
Automation offers workflow potential, too. Once validated terms are in production, rules-based engines can execute payment waterfalls or calculate accruals with less manual intervention. With AI-enabled automation, you can route incoming documents by type and complexity. Routine updates flow straight to production, while interpretive changes reach a reviewer with the relevant prior terms and impact analysis already assembled.
Loan practitioners are applying AI to automated document abstraction, covenant monitoring, exception resolution, interest and fee validation, and trade break analysis inside existing workflows and best practices. Agentic capabilities push that further by shortening the time between an amendment and the right person seeing data in context.
Amendments make the challenge of automating operational data even more acute. A change in underlying terms can force your team to reconfigure and retest from the ground up before putting data into production. For example, a covenant provision might be expressed in an operational platform as a numerical threshold. If an amendment alters aspects of the covenant, the factors relating to the provision could be expressed in qualitative language. Someone has to reformulate the way risk and compliance are tested going forward.
Changes at that level cascade across servicing, monitoring, risk, and reporting. Changes to covenant testing can affect your monitoring team’s ongoing assessment of the borrower, or collateral shifts can lead you to adjust how you measure risk and revisit the accounting treatment of the loan.
Credit monitoring is another area where automated data extraction reaches its limits. When you run borrowing base calculations or check covenant thresholds, you use loan data that’s already in your systems. You need more than operational data to assess whether a borrower is heading toward potential trouble before a default. Your monitoring team reads the portfolio company’s financials and weighs them against industry conditions. Their view of the situation determines whether there are signs of distress. It’s a repeated process supporting every reporting cycle and, like interpreting amendments, requires judgment after data is adjusted.
The operational question is how your team coordinates review of incoming documents, amendments, new borrower financials, or updated collateral schedules. There isn’t a standard operating model for private credit like there is for other asset classes. Some organizations use purpose-built technology and automate aggressively. Others rely on headcount.
Where your operating model lands depends on your portfolio composition and data infrastructure. Workflow efficiency is determined by how well you handle data and manage variations across facility types, amendment frequency, and the proportion of routine versus interpretive work. A portfolio of mostly stable term loans is a different design problem than one with active revolvers, frequent covenant amendments, and high borrowing base activity.
The approach to operating model plays out across your organization. Portfolio managers, risk teams, and auditors all consume the same loan data that your operations team produces. It also feeds into the fund-level accounting output used for P&L. When that production process depends on side channels and institutional memory rather than traceable workflows, the gaps surface.
“Lending and private credit markets have reached new heights, but agents are still managing billion-dollar deals with Excel and email, creating massive operational risk.” — Darren Thomas, Head of Enterprise Solutions, S&P Global Market Intelligenceii
Those gaps become more consequential when you look to weave AI into your operating stack. If the boundary between rules-based processing and interpretive judgment lives in someone’s head rather than in a defined workflow, you run the risk of AI trying and failing to come up with its own interpretations. A model trained on historical amendments could flag patterns, but it wouldn’t determine whether a specific covenant change affects the loan’s risk profile. Your operating model has to answer that question before you put AI to work.
Making the boundary explicit strengthens both deterministic and interpretive work. Mechanical work runs faster and more consistently when you separate it from what needs human judgment. Judgment calls carry more weight when you can review what they are and how people made them.
Where and how you draw the operational line between automation and judgment determines how confidently you can scale. The organizations getting this right are making that decision explicitly before they add AI to the stack, not after it’s already doing the wrong thing.
Self-assessment
Use these questions to assess where your operating model currently draws the line and where it leaves the boundary implicit.
Jeb Altonaga
Jeb recently joined Arcesium in a business development capacity focused on Private Markets, leveraging his extensive experience to deepen engagement within this fast-growing segment of the alternatives landscape.
In 2021, Jeb founded Clearglass Capital Partners, a private capital advisory firm supporting financial sponsors and institutional investors in capital formation and strategic initiatives. Previously, he served as COO of Sandon Capital in Sydney and Partner & COO of Blue Pool Capital, chairing the firm’s Valuation and Operating Committee where he also held fiduciary roles as Director of the Investment Manager and its Cayman funds. Earlier in his career, Jeb was with Citadel, later relocating to Hong Kong.
Jeb holds an MBA from NYU Stern and has served on the Board of Hedge Funds Care, Asia (HFC), where he chaired the Grants Committee supporting child protection initiatives across the region
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