Corporate finance training data, built by working analysts.
A model can define enterprise value perfectly and still get the valuation wrong on step four. eeko commissions credential-checked bankers, research analysts, private equity associates, and FP&A professionals to build the models, write the problems, and grade the reasoning your financial AI learns from. Every item fingerprinted, second-reviewed, and licensed.
What is corporate finance training data?
Corporate finance training data is the material a financial model learns from and is tested against: a DCF built from scratch with every assumption written down, a capital structure problem with a worked solution, a set of filings with the periods and non-GAAP adjustments made explicit, a model's valuation graded step by step to find where the error entered and how far it carried. If that material is textbook problems scraped from the web and graded on the final number, the model learns to look right rather than be right.
The failures are specific. Fiscal and calendar periods mixed, a non-GAAP figure treated as reported, cash double-counted in a bridge, a sign flipped in the cash flow statement, a small arithmetic slip carried through twelve rows of a model. eeko's data practice commissions credential-checked finance professionals to build and grade against the standard a managing director would apply, with a second expert confirming each item.
This page belongs to eeko's human-made AI training data practice, built because the analysis and document systems we deliver for banks and asset managers (see our financial services AI work) need data nobody has published. See also training data for financial services and how eeko verifies human data.
Where finance models go wrong, and what fixes it.
Three failure patterns from financial model evaluations, and the data decision behind each fix.
Financial reasoning datasets and valuation models, to your spec.
Six request types we fill for financial AI teams, each one starting with a spec, then a pilot, at a fixed per-item price.
- Sector and complexity to order
- Delivered as spreadsheets plus structured exports
- Fingerprinted against contamination
- Built for supervised fine-tuning data
- Schema drafted with your team
- Part of our document datasets
- Error taxonomy agreed up front
- Feeds RLHF preference data
- Refreshed on a standing order
- See AI evaluation benchmarks
- Build, audit, and repair tasks
- Pairs with our enterprise AI agents work
From written spec to signed delivery.
Pilot before volume, fixed price per item, and a finance professional on both ends of every item.
Every model, provable.
Financial AI gets reviewed by model risk and compliance. The data behind it should survive the review.
What travels with each item
- Fingerprint — a cryptographic hash taken on arrival and compared across the set for duplicates.
- Signed provenance — who built it, whether a model proposed any part, and which expert confirmed it.
- Independent timestamp — proof of when the item existed, from a source we do not control.
- Consent and licence — recorded per item, so you can show what each model or problem may be used for.
- Privacy and origin checks — spreadsheets and documents screened for personal information; generator signatures and C2PA or IPTC fields detected.
- Audit trail and receipt — hash-chained history, and a signed receipt naming every item in the batch.
It is the same discipline behind our own compliance and governance work.
Paid remote AI work for finance professionals.
The modeling and analysis you already do, from anywhere, with the rate on the job before you take it. No clients, no recommendations, nothing published under your name.
The work itself
- Build reference models — build a valuation or operating model to a brief, writing down every assumption and formula as you go.
- Write problems with solutions — draft professional-level finance problems and the step-by-step answers a model should reproduce.
- Grade model reasoning — score a model's calculation line by line and pinpoint the step where it went wrong, and why.
- Second-review a peer — audit another expert's models and grades before the batch is delivered.
Jobs come to you when your verified field fits, and every one states its scope, deadline, and rate. Start at apply as an expert, or read how the program works on expert data annotation jobs.
Identity and credentials are confirmed once, before any job is offered. After that, a peer reviews your work, not a generalist QA team.
- Degree Finance, economics, accounting, or a quantitative field
- Experience Years spent in banking, equity research, PE, corp dev, or FP&A
- Credential CFA charter or progress toward it, or an MBA with a finance focus
- Modeling Hands-on three-statement, DCF, or LBO work
- Sector Energy, banks, insurance, technology, and others welcome
You see the rate and the scope, then decide. Approved work is credited and paid out with an itemized record.
- Transparent Rate on every job before you accept
- Remote Your schedule, inside the deadline
- Reviewed Second expert approves, payment releases
- Private Never named on delivered data
Related fields, solutions, and industries.
Corporate finance training data questions.
What is corporate finance training data?
Corporate finance training data is the material a financial model learns from and is tested against: a DCF built from scratch with every assumption written down, a capital structure problem with its worked solution, an annual report with the periods and adjustments made explicit, a model's valuation graded step by step to find where the error entered. At eeko it is built and reviewed by credential-checked finance professionals, and every item ships with a fingerprint, a provenance record, and a licence.
Who builds the financial models?
Finance professionals whose credentials we have checked: analysts from investment banking and equity research, associates from private equity, people in corporate development and FP&A staff, and CFA charterholders. All of them clear identity and credential checks, and many sit a short, paid assessment. A second expert reviews every model or grade before delivery, and label provenance names the builder and the reviewer.
Is this work financial or investment advice?
No. Experts build and check data for software. Nothing they produce is a recommendation to anyone, and nobody becomes a client. Depending on your specification, problems and models can feature real listed companies or invented ones, and in either case they become training or evaluation material and nothing else. Your name is never attached to delivered data.
How are finance professionals paid for AI training work?
Every job states its rate before you take it, and there is no minimum commitment. Once a second expert approves your model or grades, the work is credited to you and paid out, with an itemized record of what was approved. Identity and tax details are confirmed once, at onboarding, so payment is not delayed later.
Can I take this work while employed full-time in finance?
Most experts in the network do. Jobs come to you when your verified field fits, every one stating its scope and deadline, and you pick the ones you want. Everything is remote, so it slots in around deal timelines or reporting cycles. Check your employer's policy on outside work and any restrictions on discussing public companies before accepting your first job.
How much does analyst-built finance data cost?
Pricing is per item and depends on the task, the sector, and how much second-expert review it needs. A single step-level grade costs far less than a three-statement model built from a brief with every formula documented. We quote a fixed price for a pilot batch after reading your brief, so you can check the standard before scaling. Exclusive licences are priced separately, and a standing order can keep fresh problems arriving for held-out evaluation.
Finance data a managing director would sign.
Send the topic, sector, and the item types you need. A real person replies inside one business day with questions and a pilot price. Analysts can apply in minutes. No deck. A ranked list.
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Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: