By Helle Pilia, Head of Client Service Delivery, Loan Administration
“Perhaps the biggest opportunity presented by AI is not finally making every loan look the same, but making it less important that they do not.”
Is AI transforming the loan market, or changing how we manage its long-standing complexity?
This question came into sharp focus during a recent industry roundtable I took part in at the Loan Market Association (LMA)’s annual conference. The discussion reinforced something I’ve been observing across loan administration and client service delivery: artificial intelligence (AI) may not eliminate complexity, but it can help us manage complexity more effectively.
The question beneath the technology
AI is increasingly presented as a route to modernising the loan market. The potential is clear. AI can read credit agreements, extract terms, structure information and make large volumes of loan data easier to interrogate.
But I keep coming back to a more fundamental question: is AI solving one of the loan market’s underlying problems – the lack of standardised data – or is it providing a more sophisticated way of working around it?
I do not think the answer is as simple as saying that the market needs more standardisation.
The problem beneath the data
Anyone who has worked in loans knows this has never been a particularly clean data environment. Credit agreements are complex. Structures vary. Terminology differs between transactions, jurisdictions, borrowers, lenders and law firms. Amendments build up over the life of a loan, while operational data often sits across different systems and organisations.
In practice, the challenge is not simply extracting information. Information must be taken in, made usable within an operating environment, and then returned to clients, many of whom want that data within their own platforms. Private credit adds another layer because both the assets and the reporting requirements can be highly customised.
The industry has worked on common documentation and market practices for many years. Those frameworks are useful, but a framework is not the same as making every loan identical. Nor should it be.
Standardisation has a natural limit
A loan, particularly in private credit, is not simply a financial instrument that happens to be recorded inefficiently. It is the outcome of a negotiation between a borrower and its lenders.
Pricing, covenants, reporting obligations, permitted actions, security and many other provisions reflect an agreement reached between two sides at a particular point in the credit cycle. That relationship and flexibility have value.
If every aspect of the product were standardised simply to make downstream processing easier, the market could lose some of the flexibility that makes private credit attractive. The better objective may be to standardise the information about the loan while preserving the ability to negotiate the loan itself.
Loan terms reflect the balance of bargaining power
Loan documentation also reflects where the market is at a particular moment. Negotiating power moves between borrowers and lenders, and the terms agreed will move with it.
Private credit illustrates this well. One of its traditional distinctions from the broadly syndicated market has been the continued use of financial maintenance covenants. As private credit has grown and moved further up the market, competition has increased and some larger transactions have adopted more covenant-lite features.
That does not mean private credit is simply becoming the syndicated market. It shows that documentation develops with the size and quality of the borrower, the lender’s risk appetite, the competitive environment and the negotiating position of the parties.
Importantly, bespoke documentation should not automatically be viewed as inefficiency. A covenant is not only a legal protection. It can also be an early-warning mechanism and a framework for engagement between a borrower and its lenders. Its value often comes from being tailored to the business and the risks of the transaction.
This is the distinction that matters for AI. We should not confuse variation caused by poor data practices with variation created by a deliberate commercial negotiation. AI should help us manage the former without requiring us to eliminate the latter.
What changes when AI can read the variation?
It’s tempting to conclude that placing an AI layer over inconsistent documentation merely makes the existing process more efficient without fixing the underlying problem. I think there is some truth in that, but it’s not the whole story.
Traditional technology needed information to be highly structured before it could process it reliably. People therefore translated the complexity of a credit agreement into fields, templates and system records. AI can now interpret prose, recognise different drafting approaches and extract information from more complex clauses.
That is a meaningful change, but of course doesn’t remove the need for control. Just because a model can produce an answer doesn’t mean it’s sufficiently reliable for the intended purpose.
The level of assurance required will differ depending on whether the output supports servicing, accounting, risk management, a credit decision or a legal obligation. Experienced professionals still need to decide what requires review, challenge results that do not make commercial or operational sense and understand the consequences of getting the interpretation wrong.
AI changes the work, but not the need for expertise
Loan administration involves so much more than moving data from a document into a system; it requires interpreting negotiated terms, understanding how they apply over the life of a facility, identifying exceptions and connecting information across notices, amendments, borrower activity, lender positions and client reporting requirements.
An AI model may locate a clause or extract a data point, but it doesn’t automatically know whether an amendment has changed the original position, whether the result is consistent with the wider agreement or whether the operational treatment creates a downstream impact for accounting, cash management, compliance or reporting.
That is where human expertise becomes more valuable, not less. Experienced loan professionals know what to question. They recognise when an answer looks technically plausible but commercially wrong. They understand market practice, transaction history and the significance of an exception. They can also explain the position to clients and other stakeholders and take responsibility for the outcome.
Rather than removing people from the process, the objective should be to remove avoidable manual effort and give skilled teams better access to the information they need. The role becomes less about searching and rekeying, and more about validating, interpreting, connecting and acting.
For service providers, this distinction is fundamental. Technology can improve scale and consistency, but trust will continue to depend on accountable people who understand both the documentation and the operational consequences. AI may change how the work is performed, but human expertise remains part of the service being delivered.
From document extraction to data discovery
AI could also change which information is worth capturing. Historically, firms extracted a limited set of fields because collecting anything more was expensive. If AI reduces that cost, users can ask for information that was not routinely digitised at all.
Instead of capturing only what is needed to book and service an asset, firms may be able to create a richer information layer for portfolio management, risk, investment teams, operations and clients.
The question shifts from “Which fields are important enough for someone to capture manually?” to “What information exists in our documentation that we have not previously been able to use at scale?”
Human judgement remains important here too. Technology may surface more information, but experienced practitioners still need to determine which terms matter, how they interact and what they mean for the ongoing administration of the loan.
The real opportunity may be interoperability
For me, the opportunity has to be considered front to back. AI cannot become another isolated point solution.
Lenders, managers, administrators, agents and borrowers already use different systems. If every AI product interprets and outputs the same information differently, the industry may simply create another integration problem.
In that sense, AI makes standardisation more important, not less. The key is to be precise about what needs to be standardised.
Legal agreements don’t need to become identical and commercial negotiations should not be flattened. What increasingly needs commonality is the information layer: clear definitions, reference attributes, identifiers, schemas and interoperable ways of exchanging data.
AI can help bridge the gap between negotiated documentation and structured data, but only if the information can move through the wider operating environment.
A sticking plaster can still create value
So, is AI merely a sticking plaster? In one sense, perhaps it is. AI doesn’t remove bespoke documentation or align every organisation’s data architecture.
But eliminating those differences may be the wrong ambition. A technology layer that helps systems understand legitimate variation may be more valuable than forcing the market to eliminate that variation altogether.
I see the future market operating across two connected layers. The commercial layer remains flexible, allowing borrowers and lenders to negotiate pricing, protections, covenants and structures according to the credit, the relationship and market conditions. The information layer – where AI has a bigger role to play, in my view – becomes more consistent, allowing technology to identify, interpret and exchange what those negotiated terms mean.
AI vs standardisation is not an either/or situation
The industry does not need to choose between standardisation and AI. Rather, decisions need to be made on what should be standardised because variation adds no value, and what should remain bespoke because the variation is fundamental to the relationship between borrower and lender.
Common standards should be used where they improve interoperability, transparency and efficiency. AI should be used where complexity and variation are unavoidable. Experienced people must remain accountable where judgement, validation and client responsibility matter.
In loan administration, the strongest model is likely to be one in which AI reduces the effort spent locating, comparing and structuring information, allowing specialists to spend more time applying judgement, managing exceptions, protecting data quality and supporting clients.
That feels like a more realistic vision of modernising the loan market than trying to force an inherently negotiated product into a completely uniform model.
Perhaps the biggest opportunity presented by AI is not finally making every loan look the same, but making it less important that they do not.
How IQ-EQ can help you turn loan data into better decisions
AI is only as effective as the data and operational processes behind it. At IQ-EQ, we combine specialist loan administration, loan agency and reporting expertise with technology-enabled solutions to help clients improve data quality, streamline workflows and unlock more value from their loan portfolios.
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About the author
Helle Pilia is Head of Client Service Delivery for Loan Administration at IQ-EQ. She has extensive experience in loan operations and client service, working with clients across a broad range of loan and credit strategies. Her focus is on building effective operating models that combine specialist expertise, strong data and technology to support efficient and scalable loan operations.
