AI that summarizes earnings, drafts client notes, or explains transactions has one unforgivable failure: getting the numbers wrong. Financial AI evaluation grades every figure, every claim, and every disclosure against your source data.
We grade outputs. We grade your AI's outputs against your data and compliance rules. We do not provide licensed financial professionals, and our grading does not replace professional review.
Financial AI output evaluation is the strict grading of AI-generated financial content, summaries, client communications, internal analysis drafts, against your source data and compliance rules. Fintech and finance teams need it because a fluent paragraph with one wrong number reads as authoritative right up until someone acts on it. Good looks like every figure traceable to source data, calculations that check out, required disclosures present, and no invented market facts. Plainly: we grade outputs. We are not licensed advisors and our reports are not financial advice.
Financial AI fails at the exact point where precision matters. We grade for the patterns that cost the most.
The most dangerous financial AI error: a figure that is close enough to look right. Revenue of 4.2 million instead of 2.4. A rate quoted one decimal off. We grade every figure in the output against your source data, not just the narrative around it.
Models fill gaps in market knowledge with plausible fiction: a merger that never closed, a rate decision on the wrong date. In a client-facing summary, that fiction becomes your firm's statement. We check factual claims against your approved sources.
Financial content often needs specific disclosures, risk language, or disclaimers. Summarization and rewriting drop them silently. We grade outputs against your required-disclosure list, so a missing line is a flagged failure, not an accident you find later.
There is a line between explaining data and advising action, and models cross it casually. We grade whether outputs stayed on the analysis side of that line per your compliance rules, especially in client-facing content.
You send 20 to 50 outputs plus the source data and compliance rules they should reflect, or we help you write a rubric from them.
Trained reviewers grade every sample against your data and rules, backed by automated checks on figures and disclosures. Wrong numbers are P0s.
Sample-level grades, figure-level error tracking, the failure patterns across the batch, recommended fixes, and a walkthrough call.
Reviewers verify every figure in the output against your source data: the statements, the tables, the market data it was supposed to reflect. Automated checks back them up on totals and percentages. A single wrong number fails the sample no matter how good the prose around it is, because in finance the number is the product. Our severity scale treats a wrong figure as a critical failure, not a typo.
Required risk language and disclaimers do not get partial credit. They are either present and correct or they are a flagged failure. The report lists disclosure gaps separately from accuracy issues, so your compliance team can clear their half of the report without reading ours.
We also check the narrative around the numbers. A correct figure wrapped in a misleading explanation still misleads, and clients act on the explanation as much as the digit. Reviewers grade whether the commentary the AI adds is supported by the data, so a right number with a wrong story does not slip through on the strength of the digits alone. This is where many financial AI systems quietly fail: the table is perfect and the takeaway is wrong. The report treats narrative accuracy as its own graded dimension, separate from figure accuracy, because both have to hold.
No. We grade AI outputs against the data and rules you provide. We do not provide licensed financial professionals, and our reports are not financial advice and do not replace review by qualified professionals. What you get is a documented read on where your system's outputs break your own standards.
Against the source data you provide: the statements, the tables, the market data the output was supposed to reflect. Reviewers verify figures claim by claim, and automated checks back them up on things like totals and percentages. A wrong number fails the sample regardless of how good the surrounding prose is.
Earnings summaries, portfolio commentary drafts, client email drafts, transaction explanations, KYC and onboarding document summaries, internal research notes. If the output makes claims about money and your team can define correct, we can grade it.
Yes. Your compliance requirements become explicit rubric checks: required disclosures, forbidden claims, approved language, advice boundaries. The report shows compliance gaps separately from quality gaps, so your compliance team gets what it needs.
Analyst review is valuable and inconsistent as a measurement system. We give you the systematic layer: every sample graded the same way, figure errors counted, patterns named. Your analysts then spend their time on the judgment calls, not on re-checking the same wrong total.
Hallucination detection asks whether outputs contain invented facts. Financial AI evaluation adds the money-specific layer: figure-level accuracy against source data, disclosure compliance, and advice-boundary checks. For financial content, a right-sounding paragraph with one wrong number is the failure that matters most.
Send 20 to 50 outputs with your source data. We will grade them all.
Not sure where to start? Talk to us and we will point you at the right evaluation.