Financial AI data, built by people who do the work.
Financial models are wrong in expensive ways: a misread footnote, a miscalculated ratio, a recommendation that ignores suitability. Expert-made data is how you find and fix those errors.
Financial services, with proof.
Banks, asset managers and fintech teams use models to read filings, extract figures, summarize earnings calls, answer client questions and support analysts. These tasks combine reading, arithmetic and judgment, and models often fail at the seams: they quote the right number from the wrong period, or reason correctly from a misread table. HUMXN commissions credential-checked analysts, accountants and finance specialists to write demonstrations, label documents and grade model answers to your rubric. You define the workflow. We scope the dataset and deliver a pilot batch first.
Financial reasoning is checkable, so the data should be checked. Our experts show their work: the source line, the calculation, the assumption. Graded answers record who made each call, labels are marked by source, and a second expert reviews disputed items. Held-out evaluation sets, such as multi-step questions over realistic statements, are written for you and exclusively licensed, so your benchmark is not measuring how much of a public dataset leaked into pre-training.
Financial documents contain account numbers, names and addresses. Documents and tables are scanned for personal data and masked before delivery, and personal metadata never reaches buyers. Each item carries an Ed25519-signed provenance record in a hash-chained audit trail, with a signed receipt listing every item by ID and hash. That record is designed to support the model risk management and documentation work your teams carry out under supervisory expectations and rules such as those from FINRA, without standing in for their review.
Built to your specification.
Every engagement starts from a written spec and a pilot batch. These are the most common requests we source for financial services.
Financial reasoning traces
Step-by-step analysis of statements, ratios, valuations and scenarios, written by analysts with every source and calculation shown.
Document extraction labels
Line items, tables, footnotes and covenant terms extracted from filings and reports, labeled and reviewed by accountants.
Graded model answers
Rubric grading for numerical accuracy, source support, suitability and appropriate caveats, with written rationales.
Client communication demonstrations
Expert-written explanations and responses for fictional client scenarios, for assistants that must be clear without overstepping.
Structured financial tables
Clean, labeled tabular data in CSV or Parquet, with personal data scanned and masked before it reaches you.
Held-out finance evaluations
Never-published, multi-step questions over realistic documents, exclusively licensed for regression and release testing.
01Arithmetic and sourcing errors
A plausible number from the wrong period is still wrong. Expert-shown calculations and source lines teach models to be traceable.
02Personal and account data
Documents and tables are scanned for personal data and masked before delivery, so sensitive details stay out of training.
03Model risk documentation
Validators ask what a model was trained and tested on. Signed provenance and per-item receipts provide a clear record.
Financial services: asked often.
What financial tasks can your experts cover?
Statement analysis, ratio and valuation work, document extraction, accounting treatment questions, risk scenarios and client-facing explanations, among others. During scoping we match the tasks to the right specialists, such as accountants for extraction and analysts for valuation reasoning. Every expert is credential-checked, and the pilot batch shows quality before you commit to volume.
How is sensitive financial data protected?
Documents and tables are scanned for personal data and masked before delivery, and location and personal metadata never reach buyers. Many finance datasets are built from realistic fictional scenarios, which avoids customer data entirely. Every delivered item is listed on a signed receipt by ID and hash for your own data governance records.
Does this meet our regulatory requirements?
We do not offer regulatory guarantees or legal advice. Our provenance records, audit trail, consent and privacy controls are designed to support the documentation and model risk work your teams carry out. Whether a dataset meets your obligations is for your compliance and legal teams to decide, and we provide the records they need.
How do you verify numerical accuracy?
Experts show their sources and calculations, so reviewers can check each step rather than just the final figure. Disputed items go to a second expert, and labels record whether they came from a creator, an AI suggestion or expert review. Acceptance criteria for accuracy are set in the spec and confirmed in the pilot.
How is pricing determined?
By task complexity, seniority, time per item, review depth and licence. A multi-step valuation with full working costs more than a single extraction label. Exclusive licences add cost; volume and standing orders reduce it. We quote after scoping, once the spec is written.
Tell us what your model needs to learn.
Send a brief in five minutes. A data lead replies within one business day with questions and a first sourcing plan.