Accounting AI training data and evaluation

Choose a workflow before choosing a dataset

A clear task makes the evaluation useful. Define what the model receives, what it must produce and when it should ask for more information.

• Journal-entry review: identify missing context and explain proposed corrections.
• Reconciliation: trace differences between records and document unresolved items.
• Financial analysis: check calculations, assumptions and evidence.
• Policy application: evaluate an answer against the supplied policy and reporting context.

Define the review criteria

Agree on the accounting framework, jurisdiction where relevant, reporting period and permitted sources. Separate arithmetic accuracy from the quality of the reasoning.

• Correctness of calculations and treatment.
• Support for conclusions and traceability to evidence.
• Handling of incomplete or contradictory information.
• Consistency between the final answer and its explanation.

Keep evaluation material separate

Plan a held-out evaluation set and document how it differs from training material. Compare models on the same tasks and criteria so a change in score is interpretable.

• Agree on task categories and coverage.
• Document reference answers and reviewer decisions.
• Record disagreements and how they are resolved.
• Report limitations alongside results.

Scope the deliverable

A project can include tasks, reference answers, corrections or preference judgments. Agree on the format, review process and acceptance criteria before work begins.

• Provide the target workflow and examples of current failures.
• Specify whether you need new tasks, review of authorized material, or both.
• Define the output schema, timeline and security requirements.
• Confirm task volume and expert availability during scoping.

Common questions