Financial services LLM fine-tuning data

This page focuses on data design for fine-tuning financial models. For the broader view of finance and accounting AI data, see finance and accounting AI training data; for how fine-tuning purchases fit together, start at the fine-tuning and post-training data hub.

Why public financial datasets leave a gap

Public financial datasets mostly teach a model to read disclosures, not to do the work of a finance team. FinanceBench, for example, poses questions over public company filings [1], FinLoRA benchmarks LoRA fine-tuning across 19 financial datasets including four XBRL analysis sets [2], and SECQUE evaluates analyst-style questions over SEC filings [3]. These are valuable for 10-K comprehension and XBRL tagging, and weak for the tasks most financial-services teams deploy.

The missing material is internal: a cash application where the remittance advice does not match the invoice, a month-end accrual memo, a break investigation on a custody reconciliation, or a reply to a client disputing a fee. Those exist only inside banks, broker-dealers, lenders, fintechs and corporate finance departments. Training on EDGAR-derived text also raises contamination risk, because the evaluation sets you would use to measure progress are built from the same filings [1][3].

Record types worth licensing, by training objective

The record type should follow the objective: supervised fine-tuning needs expert outputs, RLVR needs checkable targets, and extraction needs gold fields. The table maps common financial operations records to the objective they serve best.

For extraction targets, the patterns in structured-output fine-tuning data apply directly; for spreadsheets and trial balances as model inputs, see tabular data for LLM fine-tuning. Transaction-flow records such as purchase orders and payments are covered in procure-to-pay and order-to-cash records.

Numeric fidelity: de-identify without breaking the arithmetic

De-identification of financial records must leave every total, balance and cross-reference arithmetically intact, or the model learns wrong arithmetic. A common failure is a redaction tool that treats any long digit string as an account number and masks amounts, invoice totals or CUSIPs along with it. Another is "noise" added to amounts for privacy, which makes subtotals stop summing and trains the model to accept mismatches as normal.

Ask the supplier to describe the method field by field. Good practice includes:

• Identifiers are tokenized consistently. The same account, customer or counterparty maps to the same surrogate everywhere in the dataset, so a model can still learn that two lines belong to one relationship.
• Format is preserved where the model must parse it. A surrogate IBAN or routing number keeps its length and check-digit shape if the task involves validation; otherwise a typed placeholder such as <ACCT_0412> is cleaner.
• Amounts are left exact or scaled as a whole. If amounts must be disguised, a single per-entity scale factor applied to every figure keeps ratios and sums valid; independent noise per figure does not.
• Dates shift as a block. Shifting all dates for an entity by the same offset preserves aging buckets, period boundaries and day counts, but conflicts with as-of context (see below), so record which was done.
• A tie-out check runs after redaction. The supplier should show that control totals, trial balance sums and reconciliation nets match before and after.

Memorization is the reason this matters beyond compliance: training data can be extracted from production language models by querying them [8], so a raw account number in an SFT example is a potential disclosure. Differentially private training is a complementary control; see differential privacy for LLM fine-tuning.

Point-in-time context so answers are not anachronistic

Every financial example should carry the date and rule set it was produced under, because the correct answer to a finance question changes over time. A lease treatment under a superseded standard, a fee schedule from two years ago or a tax rate before a change will each teach confidently wrong outputs if the model cannot see the as-of date.

Minimum fields to request: as_of_date, period_end, fiscal_calendar, accounting_framework (for example US GAAP or IFRS) and the relevant policy or rule version, currency and FX rate source, and the chart_of_accounts_version. When date shifting is used for privacy, keep the shift offset in a supplier-held key or keep the original period labels, so period logic still works.

Reconciled entries as verifiable targets for RLVR

Closed reconciliations are among the few financial records where correctness can be checked by a program, which makes them strong candidates for reinforcement learning with verifiable rewards. A reward function can test whether proposed matches net to zero within tolerance, whether every line is either matched or assigned a break code, and whether the break code equals the one the analyst finally recorded.

Two cautions apply. First, the population is skewed: most lines match automatically, so ask for the distribution of match types (one-to-one, one-to-many, many-to-many, unmatched) and oversample exceptions. Second, the final state is not always right; reopened items and later adjusting entries should be linked so that a reversed decision is not used as a positive target. Curation matters more than volume here, consistent with findings that a small set of carefully chosen examples can drive strong fine-tuning results [7].

Regulated client communications need review status attached

Client communications from regulated firms are useful training data only when each item carries its compliance disposition. Under FINRA Rule 2210 as of October 2026, communications are classed as correspondence, retail or institutional, and retail communications generally require approval by a registered principal before use [5]. Broker-dealers also retain communications under FINRA Rule 4511 and SEA Rule 17a-4 [6], which is why these archives exist at all.

Ask for the reviewer's disposition (approved as is, approved with edits, rejected), the edited and original versions, the 2210 category, and the reason code for rejections. Edit pairs are effectively preference data. The design is covered in compliance-reviewed communications as policy-alignment data; a corpus of approved messages only teaches tone, not the boundary.

Financial privacy limits on customer-level data

Customer-level financial records sit under GLBA in the US, and those rules shape what a supplier can release. Regulation P, which implements the GLBA privacy provisions for many institutions, limits how nonpublic personal information received from a nonaffiliated financial institution may be redisclosed and reused [4]. In practice, a supplier that holds data received from a bank partner may not be free to license it onward without checking the original terms, the exception it was received under, and whether the de-identified records still count as nonpublic personal information.

Practical questions for counsel and the supplier: is the supplier the institution that collected the data or a downstream recipient; which privacy notice applied to the customers; are any records consumer report information or card data under separate regimes; and does the data include institutional or commercial accounts, which carry fewer consumer privacy constraints. Set the permitted use precisely in the license; fine-tuning-only data licenses explains the scope options.

This page is general information, not legal advice. Confirm requirements with counsel for your jurisdiction and use case.

Example SFT record for a reconciliation break

A well-formed record keeps inputs, the analyst's reasoning, the final decision and the provenance fields together.

Illustrative example: invented to show structure; it does not describe an available dataset.

{
  "record_id": "recon-000184",
  "as_of_date": "2025-03-31",
  "accounting_framework": "US GAAP",
  "currency": "USD",
  "inputs": {
    "bank_lines": [{"ref": "<BANKREF_0091>", "amount": 18450.00, "value_date": "2025-03-28"}],
    "ledger_lines": [
      {"doc": "<INV_2231>", "amount": 18500.00, "counterparty": "<CUST_0412>"},
      {"doc": "<CM_0077>", "amount": -50.00, "counterparty": "<CUST_0412>"}
    ]
  },
  "target": {
    "match_type": "one_to_many",
    "net_difference": 0.00,
    "break_code": null,
    "rationale": "Payment equals invoice less credit memo issued 2025-03-20."
  },
  "review": {"status": "approved", "reviewer_role": "senior_accountant"},
  "provenance": {"deid_method": "consistent_tokenization_v2", "amounts_modified": false, "tieout_passed": true}
}

Acceptance checklist before you buy

Use these checks during a sample review, alongside the general process in how to evaluate a fine-tuning dataset before buying.

Illustrative example: invented to show structure; it does not describe an available dataset.

How SourceX sources financial fine-tuning data

SourceX sources operational datasets from US companies on request, including finance and legal workflows, and manages the licensing process; nothing is held in stock and a request does not guarantee a match. Each dataset is rights-reviewed for ownership and consents, personal details such as names, account numbers, emails and phones are removed or replaced before delivery with the method recorded and a sample checked, and every release is approved by the supplying company. See buyers in finance, accounting reconciliation datasets and financial transaction data, or describe your request on the buyers page.

Request financial operations records for fine-tuning

Describe the records, fields, as-of coverage and review metadata you need, not the businesses that might hold them. SourceX looks for US companies holding that data, assesses data and licensing permissions, and agrees allowed uses in a license before anything is delivered. Start a financial data request.