Why finance needs purpose-built AI solutions to avoid hallucinations

The million-dollar question is, can AI make up numbers?

Google’s Gemini-2.0-Flash-001 shows a 0.7% hallucination rate on Vectara’s leaderboard. But in Finance, it’s far from reality. None of today’s models exceed 50% accuracy, according to Vals AI’s financial benchmark.

That’s the problem with large language models your company might be using for Finance. They’re built for the masses. 

For an industry grounded in facts and data, you need specialized solutions that do more than just “fill in the blanks.”

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You’d expect AI systems to rely on facts and data, only to find out some of the numbers were made up.

That’s true! AI models don’t glitch, but they often serve up incorrect outputs in a confident, credible-sounding way, especially when asked open-ended questions. 

Ask AI to summarize financial filings, and it might confidently cite figures and references, except some of these might not exist. 

Finance teams oversee complex revenue flows, operate under the weight of regulatory compliance, and balance on the delicate thread of stakeholder trust. What might seem like a harmless mistake elsewhere can quickly turn into a compliance failure, reputational hit, and decision-making disaster for finance leaders.

AI hallucination in finance can cascade through critical business functions, and here’s how fabricated data could infiltrate your operations:

Imagine your AI misreads a performance obligation in a customer contract or “guesses” a renewal term that isn’t there. Those errors flow straight into your revenue recognition model, and suddenly, your ASC 606 numbers don’t line up. Cue the audit headaches.

Or say you’re using AI to benchmark peer disclosures in 10-Ks. If the model fabricates a citation or misquotes a filing, you could end up publishing a disclosure that regulators see as misleading. That’s not just an AI hiccup, it can turn into regulatory risks.

Controllers know that audit memos live and die on precision. If an AI slips in a hallucinated GAAP citation or misapplies a standard, the whole memo’s credibility collapses, taking your team’s authority down with it.

FP&A teams run on accuracy. But what happens if the AI invents reasons for revenue fluctuations that don’t exist? Leaders could make strategy calls based on fiction, not fact, and the ripple effects could stretch across budgets, headcount, and investor calls.

And then there’s ERP data. Finance leaders love the idea of “chatting” with NetSuite to pull live numbers. But if AI hallucinates a metric or mislabels a field, your reconciliations go sideways fast, and you’re left cleaning up a mess no one planned for.

To fight the problem, you need to understand the root cause.

Here’s the truth: AI doesn’t know facts, it predicts patterns. LLMs are trained to guess the most likely next word in a sentence. If the model doesn’t have the right data, it doesn’t say “I don’t know.” Instead, it invents something that sounds right.

Here are some common triggers of AI hallucination:

AI learns from historical data. But finance teams regularly face new market conditions or unique contract structures that simply don’t exist in training datasets. That’s where LLMs improvise and make up an answer that sounds right but isn’t really.

Apparently, large language models have a limited attention span. When they’re parsing long financial documents or juggling multiple data sources, they can lose track of key details at some point and fill in the blanks with something that feels consistent but isn’t actually true.

AI is brilliant at spotting patterns, but it often stretches them too far. In finance, that can mean inventing false relationships between numbers or projecting trends that don’t exist in the underlying data.

The way you ask the question matters. If prompts are vague, request information the model doesn’t have, or push for certainty where none exists, the chances of hallucination skyrocket, and the results are fabricated numbers.

How confidently AI delivers hallucinations disguised as responses is scary, but you can trace every false lead.

If AI gives black-and-white answers on complex contracts or accounting treatment without nuance, that’s your warning sign. Real analysis is rarely that absolute. For instance, AI tells you, “This contract clearly requires upfront revenue recognition,” without acknowledging gray areas in ASC 606.

Pro tip: Spot-check key terms directly in the source contracts or standards. Cross-check AI’s conclusions with ASC 606 guidance and your auditors for cases like this.

When AI finds the “exact” performance obligation for ASC 606 or guidance that solves your GAAP issue neatly, it’s too good to be true. Real accounting requires judgment. Like an AI verdict that says, “Performance obligations are exactly these three items,” with no ambiguity or caveats.

Pro tip: Cross-check outputs with multiple standards and interpretations.

References to FASB or SEC sources that seem tailor-made for your situation often don’t exist. Legitimate research usually involves varied, imperfect sources.

Pro tip: Verify every citation in the official FASB or SEC databases.

AI giving you exact numbers but only citing “the service agreement” without page or section detail is a red flag. For example, AI gives you a contract’s “$2.3M renewal clause expiring June 2026” but only cites “the service agreement” without page or section.

Pro tip: Ask for precise document locations, i.e., page numbers, clauses, or direct quotes.

When AI mentions amendments that don’t exist or contradicts itself if you rephrase the question, it’s fabricating.

Pro tip: Re-ask in different ways. Shaky answers often expose hallucinations.

Hallucinations may sound like an inevitable side effect of AI. The truth is, you can pretty much hold the control levers of AI hallucination with the right tools in place. Here are some changes you can make for outputs that are reliable, auditable, and regulator-ready:

AI that can’t show where its answers come from shouldn’t be trusted. Finance teams should only use tools that tie every answer back to verifiable sources, such as an SEC filing, a contract clause, or a NetSuite record. Retrieval-Augmented Generation (RAG) does exactly this, grounding responses in actual documents rather than model memory. The results are transparent answers you can verify, not guesswork you can’t defend.

Generic AI learns from internet text. You need a finance AI that’s trained on accounting standards, ERP data, and compliance frameworks. Specialization drastically reduces hallucinations because the AI understands your world from the ground up.

Controllers, auditors, and FP&A leaders still play a critical role. AI should accelerate their work, not replace judgment. Make sure every AI-driven report or memo is run through a human and reviewed thoroughly before it leaves the finance function.

The most effective way to get rid of hallucinations is to use solutions built specifically for finance. Platforms like Numero combine domain logic with audit-ready design, ensuring that outputs aren’t just fast, but grounded in real, defensible data.

If you think a custom AI wrapper can solve this, think again. It’s way more work than it seems. Stitching it together yourself is painful. What you really need is purpose-built software that actually understands finance and is designed for your use cases.

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‍Numero is a purpose-built AI that combines financial intelligence, citation-backed answers, audit-ready outputs, and built-in verification systems, making it the perfect solution for finance and accounting teams. Here’s how Numero guarantees zero hallucination:

Numero is trained specifically on accounting and finance contexts, think ASC 606, ASC 842, SEC filing requirements, and real-world contracts. This specialization means the AI understands your world the way accountants and controllers do, dramatically reducing hallucination from the beginning.

Every output Numero generates comes with direct citations from contracts, SEC filings, or FASB guidance. You’ll never be left guessing if a figure or interpretation was made up, because every insight can be instantly traced back to its authoritative source.

Numero reads and analyzes your actual documents and verified financial data, not picking up patterns and making the next guess. You can have it identify performance obligations in contracts, extract disclosures from SEC filings, or research GAAP guidance, and its answers will always be grounded in evidence.

We know finance leaders operate under strict compliance standards like SOX. Numero’s AI is trained to automatically flag missing contract terms and uncertainties in accounting interpretations. These built-in guardrails protect against errors that could slip into audits or filings.

Numero integrates directly with the tools you already use, NetSuite, Salesforce, SharePoint, and beyond. That means insights flow naturally into your existing systems, complete with audit trails and documentation that controllers and external auditors can rely on.

CFOs, controllers, FP&A teams, and audit professionals can get the efficiency of AI without worrying about fabricated financial data, with Numero. We’re building a path to AI reliability that’s easy to walk, so you can stay focused on making better, faster financial decisions.

Schedule a demo to see Numero’s zero-hallucination financial intelligence in action.