Last updated: September 14, 2026
Death by PDF costs a private credit fund in four places: analyst hours, the days between a document arriving and a decision, collateral that gets sampled instead of verified, and reporting that lives in one person’s head. Document automation is usually budgeted against the first. The other three are larger, and they are where the payback sits.
Total debt to EBITDA on the compliance certificate came in at 5.9 times. The covenant maximum was 5.5.
Nobody saw it for nineteen days. The certificate arrived as a scanned PDF with the calculation on page seven, it went into a shared inbox, and the analyst who reads those had forty-one others come in that same week. The math was right there on the page. By the time a person got to page seven, the borrower had drawn on the revolver twice.
Nobody was careless. That’s the uncomfortable part.
When somebody proposes document automation at a credit fund, the number that goes on the first slide is hours saved. Hours are real, and I will price them below. They are usually the smallest line on the bill, though, and a business case built on them gets approved for the wrong reason and then judged against the wrong result a year later.
So this piece prices all four costs. How to build the pipeline itself is covered in the six-step document-to-decision method, and I won’t repeat it. Treat what follows as the invoice.
A word on where you’re reading this. KORE1 places data engineers and data scientists, including the people who end up building document pipelines inside credit funds, and I write here as a guest contributor. I don’t sell a platform. Parts of this argue for spending less on software than you had planned.
Pricing the Keying Hours
Start with the visible cost. It’s the one your CFO will ask for, and doing the arithmetic honestly shows how modest it is.
Picture a direct lending fund with 60 borrowers, 15 of them on asset-based revolvers. Every borrower sends monthly financials and a quarterly compliance certificate. The ABL borrowers add a borrowing base certificate and a receivables aging each month, a cadence the OCC’s Comptroller’s Handbook booklet on asset-based lending treats as ordinary, noting that lenders often require certificates and supporting documentation on a weekly or monthly basis. Then the annuals arrive, audited statements and budgets. Private credit carries this load by design. A Federal Reserve note on private credit’s characteristics and risks cites industry reports that direct lenders use loan covenants more often than banks do, which is what lets them watch borrowers closely in the first place.
| Document | Per year | Hours each, spread and checked | Annual hours |
|---|---|---|---|
| Monthly financials | 720 | 2 | 1,440 |
| Quarterly compliance certificates | 240 | 1.5 | 360 |
| Borrowing base certificates with agings | 180 | 3 | 540 |
| Annual audits and budgets | 120 | 4 | 480 |
| Total | 1,260 | 2,820 |
The hours per document are my working assumptions. Swap in yours. The shape survives.
That total is roughly one and a half analysts. O*NET puts the 2025 median wage for credit analysts at $83,510, so with benefits and overhead loaded on, the bill comes to something near $150,000 a year, give or take your metro, which the KORE1 salary benchmark assistant can localize if you want a sharper figure.
That is the figure a document automation vendor builds the ROI slide around. It’s also small. If it were the whole bill, plenty of funds would be right to leave the work still done by hand and spend the money somewhere else.
Nineteen Days Is a Credit Decision
Latency comes second. Nobody books it.
Every document is a photograph of a date that has already passed. A monthly borrowing base certificate describes month-end and shows up two or three weeks later. Then it waits. Somebody reads it eventually, and from then on it governs availability until the next one lands, so near the end of the cycle a draw can get funded against receivables as they stood seven weeks earlier, give or take the borrower’s reporting habits. For a stable borrower that gap costs nothing. For one whose customers have started paying at 75 days instead of 45, those seven weeks are the whole story.
The IMF’s April 2024 Global Financial Stability Report named the portfolio-level version of this, listing stale and potentially subjective valuations among the sector’s vulnerabilities. Stale is a data word. It measures how long a number sat between its source and the person relying on it.
You can measure your own. Take the last ten covenant breaches, amendment requests, or availability disputes in the book, and for each one record the day the evidence arrived and the day someone with authority actually saw it. I have run this at firms that were sure the answer was two or three days. In my experience, the first measurement comes back longer than anyone guessed, often by a week or more. KORE1’s data operations consulting for credit funds starts from the same kind of measurement.
Automation shortens exactly this gap. Extract the covenant calculation the hour the file arrives, test it against the definition in the credit agreement, and the 5.9 is in front of a person on day one. The typing saved along the way is incidental.
Sampling Is What Manual Review Looks Like at Volume
Nobody ties out every invoice on 180 receivables agings a year by hand. They sample. A reviewer pulls the largest balances, a few oddities, anything that moved since last month, and assumes the rest. Usually that’s fine. Field examiners visit periodically and go deeper, on a sample again.
Sampling is a rational response to paper. It also leaves a specific blind spot, because the fraud patterns that matter in receivables finance live in the rows nobody pulled: an invoice that was never issued, a receivable pledged to two lenders, a customer who turns out to be an affiliate. The borrowing-base review is a fraud control disguised as data entry. At three hours a certificate, it gets staffed like data entry.
Last year made the point expensive. First Brands and Tricolor, both of which filed for bankruptcy in 2025, are accused of pledging the same collateral more than once across separate financing arrangements, as Cambridge Associates laid out in its review of the two cases. I won’t guess how those cases resolve. The pattern is old, and bank examiners already look for it; the OCC’s procedures ask whether duplicate invoices are reviewed for eligibility under the borrowing base certificate.
The cost here shows up as loss severity on the one borrower in sixty whose story sat in the unsampled rows. What automation adds is modest and specific. Once every aging is structured data, the whole population can be tested each month, invoice numbers against prior months, customer names against the borrower’s known affiliates, concentrations against last quarter. Judgment still decides what an exception means. The exception just stops depending on which rows somebody happened to pull.
The Slowest Document Is the One You Haven’t Received
My largest number on this subject came from getting documents, not reading them.
For nine years I ran data science at a specialty finance investor whose underwriting depended on medical records. Those records were requested on paper, from provider offices, one request at a time. Reading them took a while. Getting them took months, and every month a file sat incomplete was a month the capital behind it waited too.
We replaced the paper requests with electronic health record integrations. Acquisition time fell 98%, from months to days, and the number of data providers we could draw on grew ninefold. The extraction work that followed was real work. It only had something to read because acquisition got fixed first.
Credit runs the same queue on different paper. The monthly package is due on day 20 and turns up on day 34, after two reminder emails. The borrower’s controller exports financials from NetSuite or QuickBooks, prints them to PDF, and sends a file your analyst then keys back into Excel. Two conversions, both lossy, wrapped around a number that was structured data the entire time. If the borrower records those numbers land on are messy too, fix that first; Colin Boothe’s guide to cleaning master data before any AI touches it covers the order.
Where the credit agreement allows it, I ask for the export instead of the PDF, or a read-only connection to the accounting system. Some borrowers refuse. Enough agree that the queue changes shape, and the PDFs left over are the ones that deserve a person. Read the record at its source whenever the source will let you.
The Analyst Who Holds the Pack
The fourth cost is a person.
In most funds I have walked into, one analyst or one operations lead knows which borrower sends the certificate with the extra tab, why the administrator’s report never quite matches the fund’s own numbers, and which draft of the quarterly memo is the one the investment committee actually saw. That knowledge is the process. One person is the database. Everybody knows it. When they take two weeks off, month-end slips. When they resign, it slips for a quarter. Firms that can’t absorb that quarter tend to add depth on the finance and accounting operations side before they buy anything.
It bites hardest in the reporting pack, because the pack faces outward. ILPA’s updated Reporting Template, released in January 2025 for implementation beginning in the first quarter of 2026, breaks out internal chargebacks and asks for partnership expenses aligned more closely to the general ledger. A pack assembled from PDFs by one person struggles to answer a follow-up question quickly. Allocators notice the delay. They rarely mention it as the reason a commitment went elsewhere.
Your reporting pack is fundraising infrastructure. Cost it that way.
The Four Costs, Side by Side
| Cost | Where it shows up | How to measure it this month | What document automation changes |
|---|---|---|---|
| Analyst hours | Spreading, keying, tie-outs | Count the documents, then time ten of them | Most keying goes; reviewers work exceptions |
| Latency | Days between arrival and a decision | Arrival date against first review date on the last ten breaches or amendments | Evidence reaches a person the day it lands |
| Sampling risk | Unpulled rows in agings and invoice detail | Share of aging lines actually tested last quarter | Whole-population tests every cycle |
| Key-person dependency | Month-end close and the LP pack | What slipped the last time that person was out | The process lives in rules someone else can run |
Where Document Automation Pays Back First
The order I use runs almost opposite to where the hours sit.
- Compliance certificates go first. Low volume, high consequence, and the calculation is already defined in the credit agreement, so the rule you check against exists in writing before you start.
- Borrowing base certificates and agings, but only after someone has written down what eligible means for each facility.
- The LP reporting pack. Slower to pay back. It is the one allocators see.
- Monthly financials, last. Highest volume, lowest value per page, and the one every vendor demo starts with.
Who Builds It
The tool you already bought probably reads a PDF well enough. What stalls these projects is finding the person who would do the work: a data engineer who can turn a covenant definition into a test, sit next to a credit analyst for a week, and build the checks that run on every file. That combination is rare.
That seat is hard to fill fast, and it tends to be temporary by nature, a few months of build followed by a much lighter run state. It suits a contract engagement better than a permanent requisition. KORE1 handles that shape of search through data engineering staff augmentation and ETL developer staffing, and keeps 92% of its placements past the one-year mark, which matters on a build where the engineer leaving halfway resets the clock. The analysts whose keying disappears don’t disappear with it. They move to the exception queue, and if that queue outgrows the team, credit analyst staffing is the other half of the plan.
Questions From the Credit Committee
Our Fund Administrator Says Its Platform Already Does This. Does It?
Partly, for its own work. An administrator’s automation exists to produce the administrator’s deliverables, the NAV, capital account statements, investor notices, and it usually does that well. It does not read your borrowers’ compliance certificates against your credit agreements, and it does not set availability. One question sorts it out quickly. Which of your credit decisions does their platform touch? The honest answer is often none.
How Do We Put a Dollar Figure on Latency?
Days first, dollars second. Take the last ten breaches, amendments, or availability disputes and record when the evidence arrived and when someone with authority read it. Multiply each gap by the exposure outstanding during it. Treat the result as the size of the window you were operating blind rather than as a loss estimate. It tends to justify the project without help.
Where Do We Sit Compared to Funds Our Size?
There isn’t a credible public benchmark for this yet. Most published figures come from software companies measuring their own customers, which is a selection problem before it is anything else. I am interviewing 25 operators at managers between $500 million and $10 billion for a benchmark that publishes later this year, in aggregate only. Until it does, compare this year to your own last year. That comparison is the one that moves a budget.
Do LPs Actually Ask About Document Handling in Due Diligence?
In my experience, yes, though almost never in those words. The questions arrive as operational ones: how long the quarterly report takes to produce, who reconciles to the administrator, what happens when that person leaves, whether loan-level data is available on request. Each is a question about the four costs above. None of them mention software.
Which Document Should We Automate First?
The compliance certificate, for most direct lenders. It arrives quarterly, so volume is manageable while you learn. Its calculation is defined in the credit agreement, which gives you a validation rule for free. And a breach discovered late is the most expensive item on this list. ABL-heavy books are the exception, where the borrowing base certificate goes first, provided eligibility has been written down.
Time One Certificate, Start to Finish
Pick the next compliance certificate due in. Just one. Write down three times: the minute it lands, the minute someone opens it, and the minute its covenant result reaches the person who could act on it. That is your latency, measured once, on real paper, and it is a plain number nobody can argue with.
If the number surprises you, send it to me. I collect them. Connect with me on LinkedIn. Should the fix turn out to need a data engineer your team doesn’t have, KORE1 can scope that seat with you.
