Private Credit Operations Consulting for Work Still Done by Hand
We rebuild the data under month-end, borrowing bases and LP reporting, so the close stops eating most of the month.
Private credit operations consulting rebuilds the data work behind the month-end close, borrowing base reviews, covenant tests and LP reporting, so a fund stops running on spreadsheets and on the one analyst who knows where everything lives. KORE1 pairs that rebuild with the finance and data people who keep it running, and KORE1’s twelve-month retention on placements sits at 92%.
Last updated: September 15, 2026
Most of the funds that call us manage from roughly $500 million up to $10 billion. Direct lending, asset-based lending, factoring, NAV facilities. Sometimes all four. They have a fund administrator, a loan system, and a spreadsheet that quietly does more work than either.
Borrower packages arrive as PDFs, somebody keys them, the administrator’s numbers disagree with the fund’s, and the pack reaches the investment committee late. Every month. It’s an operations problem with a data problem inside it, which is why this work sits in KORE1’s accounting and finance consulting practice next to the people who staff it. Buying another platform rarely moves the calendar.
What Fills a 26-Day Private Credit Close
An illustration built on a real result. KORE1 consultant Khurram Tehseen took one firm’s month-end close from 26 days to 3. The split across stages below is the pattern we usually find, not that firm’s own log.
One month-end close, before and after the data under it was rebuilt
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- Waiting on borrower and administrator files7 days
- Keying PDFs into workbooks6 days
- Tying out to the administrator5 days
- Marks, accruals and review4 days
- Building the committee and LP pack4 days
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- Files arrive structured and get validated the day they landDay 1
- The team works only the flagged exceptions and the marksDay 2
- The pack is built from validated records and signed offDay 3
- Handed back to the team every month23 days
Look at the first row. Nothing has gone wrong yet and a week is already gone, because the close can’t start until the last borrower package and the administrator’s report arrive. Keying comes next. Then the tie-out, where two sets of books spend five days disagreeing about the same loans.
Waiting and keying are the two stages a rebuild removes outright, by asking borrowers for system exports instead of PDFs and checking each file the day it arrives. Khurram’s costing of manual PDF handling in private credit puts numbers on the hours, and a separate piece covers where AI fits in an accounting close. The days cost more.
Replay a Finished Month-End Before Automating Anything
Every engagement starts with a month that already happened. We take one completed close, with the files the team actually received and the pack that actually went out, and run it again through the rebuilt process. Then we score it.
Forecasts invite argument. A replay mostly doesn’t, because the answer key is already sitting in last month’s committee pack and the scoring covers days per stage, errors, and every place the replay disagreed with what the team shipped.
- Days per stageMeasured on your own files. Nobody estimates anything in a workshop.
- Errors, both kindsThe ones the team caught by hand that month, and the ones that went out in the pack anyway.
- One thing runningA working automation before the four weeks are up.
The replay sits inside a four-week Data Foundation Diagnostic, and the build itself follows Khurram’s document-to-decision build, laid out in six steps. The dashboard comes last. It only helps once the numbers under it agree.
One Record for Every Borrower
Ask five systems about one borrower and you can get five answers. Sometimes six. The loan system holds the legal entity, the administrator files it under a fund code, the CRM knows the sponsor, and the covenant workbook has whatever an analyst typed three years ago.
One person usually knows how they connect. That person is the database. Khurram’s definition of a data foundation is short. One canonical record per borrower, a validation library that says which fields are trusted, and a visible uncertainty tag wherever a field can’t be trusted yet.
Downstream work gets easier. Reconciling to the administrator turns into comparing two lists. Borrowing base certificates get tested against every invoice on the aging. Loan-level questions from lenders and LPs get answered from the record instead of from memory.
Outsiders run into the same gap from the other side. A Federal Reserve note on bank lending to private credit found that public disclosures or regulatory filings by private credit vehicles are generally not available, so the detail a bank or an allocator wants tends to come straight from the fund’s own records. Marks have the same lag. In a review of private credit vulnerabilities published that spring, the Financial Stability Board noted that valuations are updated infrequently, often quarterly, which holds up in normal markets and less so under stress.
Four Signs Private Credit Operations Have Outgrown the Spreadsheet
Nobody calls about data architecture. They call about one of these, usually the week after it bit.
Month-end runs past day twenty
One close barely finishes before the next month-end arrives, so the team never really leaves the fire drill.
The admin’s numbers never match
Two sets of books, one reconciliation workbook, and a week of every month spent explaining the gap to the CFO.
Borrowing bases checked on a sample
Receivables agings arrive as PDFs, so reviewers pull the largest balances and take the rest on trust.
One analyst holds the process
Only that analyst knows which borrower sends the odd file, and the fund learns what that costs the day the notice arrives.
The paper behind those calls keeps piling up. The SFNet 2025 Market Sizing Study put U.S. asset-based lending commitments at $537 billion at year-end 2024 and factoring volume near $148 billion, and every dollar of that runs on certificates and agings somebody has to read. LPs want more on top of that, and the revised ILPA Reporting Template now stands in for the 2016 version at any fund still in its investment period during Q1 2026, with fees, expenses and carried interest set out in a more uniform way.
When the gap is simply headcount, our financial analyst staffing, risk analyst staffing and treasury analyst desks cover the portfolio side, and contract accountants can carry a close while the data under it is being rebuilt.
Who Runs the Rebuilt Close After We Leave
A consultant who leaves behind a pipeline nobody can maintain has just built a new single point of failure. So the handover gets planned in week one. With names.
Two seats keep a rebuilt close running. A data engineer owns the pipelines and the validation rules, while a credit analyst owns the exception queue and decides what a flagged certificate or a strange aging actually means for the loan. KORE1 fills both, engineers through data engineering staff augmentation or an ETL developer search, and analysts through credit analyst staffing, usually on contract terms while the build settles.
Some funds also need one senior person to own data for good. That’s usually a fractional chief data officer before it’s a full-time hire.
Khurram’s test is blunt. Can the work that matters run without the one person who knows where the data is? He calls that AI readiness. It’s an operations question first and a technology purchase a distant second.
How a Private Credit Data Engagement Runs
Five steps. Only the first one feels slow, and it’s the one that makes the other four believable.
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Replay one closed month
We rerun a completed month-end on your real files and score its days and errors against what the team shipped.
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Write the borrower record
One canonical record for each borrower, plus the rules that decide which fields are trusted and which get flagged.
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03
Automate one document
Usually the quarterly compliance certificate, since each credit agreement already spells out the covenant math it gets tested against.
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Name who runs it
A data engineer for the pipeline and a credit analyst for the exceptions, both in their seats before we step back.
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05
Replay the next close
The following month-end runs through the new process and gets scored against the first replay.
Common Questions
What does a private credit operations team actually do?
A private credit operations team runs everything between a closed loan and an investor statement, including loan administration, borrower reporting, covenant and borrowing base tests, reconciliation with the fund administrator, and the LP reporting pack. At smaller managers a lot of it still runs through spreadsheets and a shared inbox.
Do we need to hire a head of data before starting?
No. A head-of-data search tends to stall for the same reason the software did, because nobody can yet say exactly what the job should fix. The diagnostic answers that first, and it often shows a fund needs a fractional data leader and one engineer rather than a full-time executive.
How long until something is actually working?
Four weeks. That covers the first working piece, because the Data Foundation Diagnostic reads where the days go and puts one automation into production inside that window. Rebuilding a whole close takes longer, and how much longer depends mostly on how many borrowers will send structured files.
Is AI accurate enough to pull covenant figures out of borrower documents?
Accurate enough on defined fields, provided each extracted value passes a rule check before anyone relies on it. An underwriting engine Khurram built for medical records ran at 99.9% decision accuracy over 50 million-plus pages, and the rule checks did most of the work behind that figure. Judgment calls buried in free text still need a person.
How is this different from buying portfolio monitoring software?
Monitoring software assumes the records underneath it already agree. They usually don’t. So the platform ends up displaying the disagreements faster. We fix the records, the rules and the handoffs first, and the tool you already bought usually starts earning its license once they’re fixed.
Can KORE1 staff the people who run it afterward?
Yes. KORE1 places the data engineers who maintain the pipelines and the credit analysts who work the exception queue, on contract or permanent terms, and has placed finance and technology talent since 2005.
What does a Close Replay need from us?
One completed month-end. That means the borrower files as they arrived, the administrator’s reports for that month, the workbooks the team used and the pack that went to the committee, plus about an hour a week from whoever ran that close. That’s the whole list.
Bring One Finished Month-End to the First Call
Tell us how many days the last close took and where you think they went. A rough guess is fine. We’ll tell you whether a replay is worth running on it.
Talk Through Your Close →