Know what breaks before you buy
Private Credit Data Buyer's Guide
Outsourced analysts, AI-only extraction and portfolio monitoring platforms all promise clean private credit data. This guide shows exactly where each one breaks, the hard-case questions worth putting to any vendor and what analyst-grade coverage should look like.
In search of clean private credit financial data
Chances are you’ve looked at or worked with a private credit data provider already. Maybe it was an outsourced analyst team, a horizontal AI tool or a post-deal monitoring platform.
Whichever option you looked at, something didn’t hold up. This guide names what breaks in each of those alternatives, sets out the problems they were supposed to solve in the first place, and lays out what a provider needs to get right so you don’t end up here again in six months.
Start with the problem(s) you’re trying to solve
Before you evaluate another vendor, get precise about what you’re solving. Data isn’t scarce, but it does show up broken in the same handful of ways every time.
Some borrowers never report on an accounting basis at all.
Pro forma has no rules, so management presents whichever view flatters most. Until someone converts it back, you don’t know true profitability or how much add-back is propping up your leverage figure.
The format changes underneath all of it.
A table shifts layout quarter to quarter, and disclosure moves between Excel, a financials PDF and a lender presentation. Any template built for last quarter breaks on this one.
EBITDA means whatever the document says it means.
Adjusted in one package, pro forma in the next, with no reconciliation between them and no guarantee either matches the credit agreement. Comparing periods means decoding each figure first.
The same company arrives under different names, and different companies arrive under the same one.
A rename after a transaction is still one company. A divestiture isn’t. Stitch the two together anyway and the chart looks clean and means nothing.
The capital structure arrives incomplete, or unreadable.
Missing structures get estimated from cash flow and balance sheet data. Multiple entities make seniority ordering genuinely hard without reading the credit documents.
You can’t build a clean time series.
Monthlies and quarterlies land with no fixed pattern, periods go missing, and a quarter sometimes surfaces 12 months later as a prior-year comparison column. Skip the reconstruction work and your history isn’t wrong, it’s just short.
What actually breaks
1. Outsourced analyst teams
They get you a human doing the spreading, but that human is usually offshore, junior and turning over faster than your coverage list.
Documents live in one system, the data lands in another, and the two rarely reconcile without someone on your team doing the legwork.
You’re also capped: seat-based capacity means scale becomes a staffing problem rather than a software one.
2. AI-only extraction tools
They move fast until they hit anything that isn’t a clean GAAP income statement.
Non-standard debt tables, pro forma adjustments, entity changes through a divestiture: these are exactly the situations where private credit reporting differs from public, and they’re exactly where extraction only tools kick the filing back to you for manual review.
The standardization work it leaves undone doesn’t disappear; it simply shifts to your desk.
3. Portfolio monitoring platforms
They’re usually built for private equity metrics like IRR and TVPI rather than credit mechanics like PIK interest or leverage covenants.
They’ll automate collection. They won’t spread or validate the financials underneath, and implementation typically runs four to eight weeks before you see any of it.
The pattern holds across all three: each one trades away data quality, pipeline integration or standardization to win on price or speed. None of them solves all three at once.
“Octus is the best place to find aggregated financial information and news on private or undercovered companies.” – BAML
What good looks like
Here’s what to hold every option against, including Octus.
On the data itself
- Analyst-grade output. Can you underwrite, model and aggregate risk directly on it, or does it only hold up in a quarterly deck? Put that question to your own analysts during a trial and take the honest answer.
- Reported and standardized, side by side. Adjusted-only output gets pulled apart by whoever needs to check the work, which defeats the purpose.
- Traceable in seconds. Pick a figure, click through to the source page, count the clicks. If it’s slow in a demo it will be slower under audit.
- A named owner for corrections. Errors happen. What matters is who owns them and how fast they clear.
On the hard cases
These are the questions worth asking, because they’re the ones a demo tends to skip. Take the failure modes above and put each one to the vendor directly.
- Pro forma conversion. When a borrower reports only a management pro forma view, do you convert it back to an accounting basis, and can you show which add-backs you stripped out and what they were worth?
- Mixed reporting frequencies. How do monthlies get rolled into quarters that compare cleanly to the quarters around them?
- Missing periods. Do you infer them from year-to-date figures and prior-year comparison columns, or do you leave gaps in the series?
- Incomplete capital structures. When a borrower doesn’t disclose the structure, do you estimate the tranches from the cash flow statement and balance sheet, or return an empty field?
- Seniority ordering. With multiple entities in the structure, who reads the credit documents to work out the actual order of seniority?
- Entity changes. How do you tell a post-transaction rename from a divestiture, and what happens to the history in each case?
A vendor that answers all has analysts. A vendor that answers three or four has a parser.
On coverage and turnaround
- The credits in your book, built on request, rather than a universe someone else selected.
- A stated turnaround on a new name, and a straight answer about what happens during a busy primary window.
- Market-level context, so a mark can be measured against the private credit market rather than only against your own book.
- Cross-market comparison, if you need it. Comparing private names to the syndicated market depends on holding a liquid credit dataset alongside the private one. Ask any vendor what’s included and what’s an add-on.
- Bespoke analytical work. Need to see fundamental trends over time based using the categorical slices and metrics you’re focused on?
- Clear pricing as your book grows, so scale doesn’t become a renegotiation.
On the pipeline
- Who fetches the documents. If you supply every file, the work moved rather than left.
- Whether the archive outlives the data room. Sites purge, and your historical comparison depends on what was kept.
- One dataset for the whole desk. Analysts, PMs, risk and the reporting team should query the same source rather than four extracts of it.
- Which access routes are live today, not on a roadmap: Platform, Excel Add-in, API, MCP.
- Permissioning granular enough for pods and strategies, with an audit trail that answers an information barrier question without an investigation.
A vendor that can’t answer the data grade, comparability and pipeline questions clearly is selling you a smaller version of the problem you already have.
Introducing Octus Private Credit Fundamentals
Accurate, in-depth, comprehensive dataf or all requested names, including direct lending and private equity deals.
Provided by Octus analysts, including reported and standardized financials, KPIs, tears sheets, complex capital structures and more.
Support built around your portfolio,
not just your data
A data feed only goes so far without context. A private credit market data file shows how a single credit performs against the broader private credit market rather than only against itself.
For your own book, our team builds bespoke analytical work on your portfolio, tracking fundamental trends using the categorical slices and metrics your team already focuses on.
That’s consistency your team doesn’t have to rebuild period over period.
What changes with Private Credit Fundamentals
Every model is cleaned, adjusted and curated by credit professionals who built these models as analysts, not an extraction pipeline retrofitted for private credit. That means every hard case question above gets answered before you ask it.
You build company modeling, forecasting and portfolio risk aggregation directly on the output, not from a side tab supporting a reporting deck.
Documents and financials sit in the same pipe. Access through the platform, Excel Add-in, or ingest via API or the Octus MCP Connector™. Using CreditAI by Octus®, every seat, from analyst to portfolio manager to risk manager, can query all your permissioned content in plain language, with links to source documents one click away.
FinDox™, Octus’ deal document management system, closes the loop between raw deal documents and standardized data. It pulls documents directly and securely from the data room, removing the manual chasing and uploading that every other provider still asks of your team.
That matters most at two distinct moments: When an auditor asks where a figure came from, the source document sits in the same system; and when a borrower reports late, the package arrives without anyone on your team going to fetch it.
Private Credit Fundamentals and Liquid Credit Fundamentals share one methodology, standardized across GAAP and IFRS. Within your private book, your team stops reconciling pro forma adjustments or mapping entities through a divestiture on its own time.
Add Liquid Credit Fundamentals and the same schema extends to your syndicated positions, so public and private credits compare on one basis instead of two sources that never quite line up.
As LPs and auditors apply more scrutiny to private credit valuations, that consistency is what you’ll need to defend a number.
Proof, not a pitch
What happens when experienced buyers test the alternatives first?
One alternative investment firm director who recently evaluated Private Credit Fundamentals noted it would fully displace Acuity analysts they’ve been using for private model builds. They said they were:
“hoping for a Honda and the Fundamentals platform is a Ferrari with all the bells and whistles,”
A credit manager overseeing more than $8 billion in credit investments, wary after a bad experience with an offshore analyst provider, tested two additional private credit platforms before choosing Private Credit Fundamentals, citing the standardization between public and private credit models and the FinDox integration as the deciding factors.
The buyers who’ve done the homework keep arriving at the same conclusion: analyst-grade data quality, standardization and one integrated pipeline are the foundation everything else in post-deal monitoring and portfolio evaluation depends on, which is why they’re hard to compare line by line and expensive to get wrong.
Switching providers doesn’t have to be the risk you’re picturing.
Set up with Private Credit Fundamentals is consumption-based and turns around in days, not the year-long onboarding a switch usually implies.
There is no seat count to negotiate and no migration project to staff. You submit a credit, Octus reports on it, and you decide from there whether the standard holds up against what you have today.
Sample private credit reporting built on your own book
Get a proof of concept, not a sales pitch.
Send us one private credit from your book. Octus will build the model, standardize it to the same methodology behind Octus Liquid Credit Fundamentals, and turn it around in days, so your own analysts can put it to the test before you decide anything.