Financial services AI training data. Every figure traced to its source line.
Models that read filings, extract figures, summarize earnings calls, and answer client questions break at the seams: a correct ratio computed on a misread table, a figure pulled from last year's column, a recommendation that forgets suitability. eeko commissions analysts, accountants, and risk specialists to write, label, and grade financial services AI training data, with the working shown on every item and account data masked before delivery.
What is financial services AI training data?
Financial services AI training data is the expert-made material a model learns from and is tested against when its work involves money: filings and reports labeled line by line, statement analysis written out with every calculation, model answers scored for accuracy and suitability, and clean tables a pipeline can load without a cleanup pass. The defining property is that the arithmetic can be audited. Somebody qualified did it, showed it, and signed it.
Banks, asset managers, and fintech teams put models to work on reading, extraction, summarization, and client conversation, and the errors are rarely dramatic. They are quiet: the right metric from the wrong quarter, a covenant threshold misread as a percentage, a plausible explanation of a product that an adviser would never be allowed to give. Public data does not contain the corrections. Expert-made data does, and a held-out evaluation set written for you is how you measure whether the corrections took.
eeko builds production systems for banks and lenders through eeko's financial services AI systems work, and this data practice grew out of those projects. It is part of the broader eeko training data practice: every item passes the same verification pipeline, gets a signed provenance record, and ships with a receipt, so your model risk team has a complete answer to what the model was trained on.
Finance LLM datasets, built to your workflow.
You describe the task and the failure you have seen. We write the spec, and these are the forms the data usually takes.
- Step-by-step, with sources cited
- Realistic invented companies
- Second-analyst review on disputes
- Field, table, and footnote labels
- Period and unit recorded per value
- Your schema, piloted first
- Pairwise or absolute rubrics
- Rationale on every grade
- Built for RLHF and regression tests
- Suitability and disclosure aware
- Tone and plain-language guidelines
- Compliance-reviewed samples
- Normalized units and periods
- Schema documented in Croissant
- Masked before delivery
- Never published, never reused
- Exclusive licence
- Refreshed on a standing order
What your validators ask, and what the data can answer.
Model risk reviews come down to one question: what did this model learn from, and can you prove it? Compare the two answers.
The data came from somewhere, labeled by someone, under a licence nobody kept. The benchmark is public, so a good score may mean memorization. The account numbers are probably still in there.
- Untraceable No record of who labeled what, or how
- Unchecked Final figures with no working behind them
- Unmasked Customer and account details intact
- Contaminated Benchmark already seen in pretraining
Each item names its author and reviewer, shows the source line and the calculation, carries a signed provenance record and a licence, and appears on a receipt. The evaluation set has never been published.
- Traceable Author, reviewer, and source on every item
- Auditable Calculations shown, disagreements escalated
- Masked Personal and account data screened and removed
- Clean Held-out evals licensed exclusively to you
Analyst-labeled training data from people who did the job.
The eeko expert network is assigned by task, so extraction goes to accountants and valuation reasoning goes to analysts.
Experts and the data types they produce
- Financial analysts — statement analysis, ratio and valuation reasoning, and scenario work.
- Accountants and auditors — extraction labels, accounting treatment questions, and table normalization.
- Risk specialists — credit, market, and operational scenario reasoning, with assumptions written out.
- Compliance specialists — suitability grading and client communication review.
- Economists — macro context, forecasting rationale, and question sets for research assistants.
Data types: statements, filings, reports, normalized tables, reasoning traces, graded answers, and earnings call transcripts. Every label carries its author and reviewer, and a disagreement between two experts goes to a third.
From workflow to delivered dataset.
Fixed scope and fixed price once the specification is agreed. A pilot batch before any volume order, every time.
Financial services AI training data questions.
What is financial services AI training data?
Financial services AI training data is the expert-made material used to train and test models that read filings, extract figures, analyze statements, summarize earnings calls, or talk to clients. It includes worked reasoning over real-looking financial documents, extraction labels on line items and footnotes, model answers graded against a rubric, and clean tabular data. The distinguishing feature is traceability: an analyst or accountant produced each item, showed the source and the arithmetic, and the item carries a provenance record and licence your model risk team can inspect.
Which financial tasks can eeko's experts cover?
Statement analysis, ratio and valuation work, covenant and footnote extraction, accounting treatment questions, risk and scenario reasoning, earnings call summarization, and explanations written for clients. While scoping we assign each task to the right specialist, which usually means accountants for extraction and treatment questions, analysts for valuation reasoning, and risk or compliance specialists for scenario and suitability work. Each expert's credentials are checked, and the pilot batch demonstrates the quality before you commit to a volume order.
How do you check numerical accuracy in the data?
By making every number auditable. Experts record the source line, the calculation, and any assumption behind each figure, so a reviewer can check the steps rather than only the final answer. Accuracy thresholds go into the specification and are proven on the pilot batch. Items where two experts disagree are escalated to a third, and each label records who produced it and who reviewed it. If your team finds an error after delivery, the audit trail shows exactly which item and which reviewer it came from.
How is sensitive customer and account data protected?
Most finance datasets we build are written from realistic invented scenarios, so there is no customer data in them at all. Where real documents are used, each document and table is screened for personal data, including account numbers, names, and addresses, and the matches are masked before anything is delivered. Location and personal metadata is stripped from every file. The signed receipt lists each delivered item by identifier and hash, which gives your data governance team a complete inventory of what entered the training set.
Will the dataset satisfy our model risk or regulatory requirements?
We do not promise regulatory compliance or give legal advice, and any vendor who does should worry you. What we provide is the evidence your validators ask for: a written specification, per-item provenance records, a hash-chained audit trail, the consent and licence terms behind each item, and a signed receipt. Those records are built to support the model documentation and validation work your teams already do under supervisory expectations. Whether a dataset meets your obligations is a judgment for your compliance and legal functions, and we give them what they need to make it.
How much does financial AI training data cost?
Price follows task complexity, the seniority required, time per item, review depth, and licence scope. A multi-step valuation with its full working costs more than one extraction label, and an exclusive held-out evaluation set costs more than non-exclusive training data. Volume and standing orders lower the unit price. We quote after the specification is written and the acceptance criteria are agreed, and the quote is fixed scope and fixed price, so the number you see is the number you pay.
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