Technology

LLMs like Chat GPT don't think, they predict. Here's how to stay smart and safe when using them in finance.

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Large Language Models (LLMs) like ChatGPT can dramatically accelerate work in finance, but they are not self-aware or intelligent in the human sense. It’s important to understand their strengths and risks before integrating them into business-critical workflows, such as reconciliation, reporting, or approvals.

Below, you’ll find practical guidance on using LLMs safely and effectively within a finance environment.

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1. Understand what an LLM is so you know its limitations

LLMs are prediction engines. They generate plausible-sounding text based on patterns in the data they’ve seen, not based on understanding, reasoning, or validation.

They can:

• interpret files and perform calculations (if code interpreter is enabled)
• identify patterns in structured and unstructured data
• communicate fluently and clearly
• assist with logic, drafting, and process design.

They cannot:

• truly validate their outputs unless explicitly told to
• recognise mistakes unless instructed to detect them
• understand domain risk (e.g. financial or legal consequences)
• know when they are wrong—they sound confident regardless.
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2. Never assume the LLM is "thinking"

Because the interface is conversational, it’s easy to assume the LLM "gets it". In fact, it only understands patterns of language, not context, goals, or consequences.

Example mistake: You expect a reconciliation tool to notice that a total is clearly wrong. The LLM doesn’t—it just outputs the formula result as if it’s correct.

Assume nothing is being "sanity checked" unless you explicitly instruct it to.
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3. Use explicit validation steps

When using LLMs in finance:

• Always verify that the total from uploaded files matches the user-provided summary.
• Stop and flag any mismatches before continuing.
• Display all reconciled items (not just a summary). Highlight any unusual values such as zero amounts, unexpected credits, duplicate invoice numbers, and totals that do not reconcile to zero.

Use logic like: "If the explained total does not match the expected difference within 1p, stop and show an error."
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4. Reduce hallucinations by providing accurate, specific data

LLMs will confidently fill in gaps if not given clear instructions and data, which is dangerous in financial workflows.

To minimise hallucinations:

• Parse real data from files using the code interpreter to turn it into structured, readable information for different formats.
• Extract data from Excel and calculate it yourself instead of relying on the LLM to understand it.
• Double-check answers instead of trusting them because they sound confident.

All outputs should be based on data, not narrative.
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5. Don’t let fluency fool you

The more professional and confident the LLM sounds, the more tempting it is to trust it.

Remember it will:

• still output an answer even when it’s unsure
• rarely say "I don’t know" unless explicitly instructed
• never flag its own hallucinations unless you build in logic to do so.

Treat it like a brilliant junior assistant: powerful, fast, and helpful, but prone to make things up when unsure and always needs a manager to set the rules and check the outputs.
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6. Use LLMs to build, not own, your process

LLMs are excellent for:

• prototyping reconciliation logic
• writing prompt chains or processing rules
• drafting communications and automating outputs.

But when you’re ready to scale or automate, consider:

• locking the logic into tools like Excel VBA, Power BI, or an internal app
• using the LLM for exceptions and explanations, but not for making decisions.

This hybrid model gives you both:

• speed and flexibility during design
• safety and repeatability in deployment.

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The takeaway rules for effectively using LLMs in finance

✅ Always verify totals before proceeding.

✅ Never let the model continue if values don’t match.

✅ Expose all calculations and lists to the user.

✅ Use LLMs for reasoning, not raw control.

✅ Remember it’s not intelligent—just extremely fluent.

When in doubt: stop, show the data, and ask the human.

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Let’s talk  

If you're looking for a hospitality finance partner who really gets multi-site ops and can support you at every stage—from daily numbers to long-term growth—we'd love to chat.

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Hello, I’m Jonna, Co-founder of Accurise. In today’s fast-paced hospitality industry, staying informed and responsive is essential. Waiting until the end of the month to assess your financial position can leave you behind the curve. That’s why, at Accurise, we present weekly flash reports to ensure our partners are always in the know, and in the now.

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These reports are powered by Excel or Power BI, informed by Xledger, our cloud-based software partner. This approach allows you to stay connected to your financials without the delay of traditional month-end reporting.

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Our partners have found that weekly flash reporting offers several advantages:

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At Accurise, we understand that outsourcing your finance department is a significant decision, particularly in a market where this concept is still emerging. However, our approach is built on a foundation of innovation, accuracy, and trust. We combine state-of-the-art technology with top-tier talent to deliver results that drive genuine growth.

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Staying Informed, Staying Ahead

In the dynamic world of hospitality, having access to accurate, real-time financial data is crucial. With our flash reports, you can rest assured that you are always informed and ready to make the best decisions for your business. At Accurise, we’re here to support you every step of the way, helping you stay in the know and in the now.

Best regards,

Jonna

Co-founder, Accurise

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