LLMs like Chat GPT don't think, they predict. Here's how to stay smart and safe when using them in finance.
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.
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.
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.
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."
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.
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.
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.
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.
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.
6 ways to work with LLMs in finance without getting burned
LLMs like Chat GPT don't think, they predict. Here's how to stay smart and safe when using them in finance.
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.
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.
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.
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."
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.
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.
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.
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.
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.
We now do payroll: Say hello to Petticoat Management Team
Same great people. Same deep hospitality know-how. Now with even more ways to support you.
We’ve got something exciting to share: Accurise has officially acquired Petticoat Management Team (PMT), our long-time payroll partners.
Founded in 1999 by Di Stevens, PMT is a BACS-approved bureau that brings 25 years of payroll experience and 150+ clients to the mix, with around 60% of them in hospitality.
Now we’re two teams with one shared focus—helping ambitious hospitality brands with hands-on finance teams, scalable tech, real-time reporting, and seamless payroll solutions.
What this means for you
With Petticoat joining the Accurise family, you get:
- a team that knows your business and the hospitality industry inside and out
- simplified operations, fewer providers, more strategic insights
- continuity and care, with Di and her daughters Jo and Amanda continuing to lead Petticoat.
As Di, founder of PMT, puts it: “We’ve always been a family-run business with a team that really understands hospitality. Joining Accurise means we can work with like-minded people and draw on more knowledge to support the businesses we care about.”
Accurise + Petticoat at a glance
- 120+ top hospitality brands (think Wingstop, SushiDog, Giggling Squid, Turtle Bay, Farmer J, and more).
- 150+ payroll clients.
- 35+ multi-site businesses and counting.
- 38% average YoY revenue growth for Accurise clients.
- 25+ years of payroll expertise from Petticoat.
A partnership 15 years in the making
Our CEO, Rob, has worked with Petticoat for 15 years—first as a client, now as a partner. “I met Petticoat 15 years ago as an operator when they did our team’s payroll,” Rob says.
“Since then, through various roles and a career change to accountancy, I’ve seen everyone out there and they’re still the best. It’s more than the tech. Petticoat takes the time to understand businesses and brings that human insight you just can’t automate. They genuinely care about getting it right, just like Accurise. Teaming up with them was an easy call.”
Powered by VIEW Group
This acquisition builds on our own momentum. In 2023, Accurise became part of VIEW Group—a Nordic leader in cloud-based accounting and IT services supporting clients across 40+ locations.
With VIEW behind us, we have access to more tools, deeper expertise, and the financial strength to keep growing, while staying true to our service-led approach.
Let’s talk
We know that running a hospitality business isn’t easy.
- Building in-house finance teams is tough (and expensive).
- Implementing best-practice systems takes time you don’t have.
- Growth can stall when you’re stuck in the day-to-day.
That’s where we come in.
Embedded finance teams. Scalable software. And now payroll. All in one place.
If you're looking for a 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.
Accurise’s secret sauce - our people + culture
Hi there,
We’re Rob and Jonna, co-founders of Accurise, and we want to share something that’s really close to our hearts—the secret behind our success. Spoiler alert: it’s not just about having the best accounting software (though, shout out to Xledger for being awesome). It’s about our people and the culture we’ve built together.
When we started Accurise, we knew we wanted to do things differently. We both come from backgrounds where the 9-to-5 grind was the norm, and let’s be honest, it often felt like a treadmill going nowhere. That’s not what we wanted for our team. We believe that happy, supported people do the best work, and guess what? It works!
A Culture That Puts People First
From day one, our mission was clear: treat people well, and they’ll take care of the rest. We’re proud to say that nearly everyone who’s ever joined Accurise is still with us. Why? Because we’ve created an environment where people actually enjoy coming to work (even if it’s from their couch in pajamas).
We’re big fans of flexibility here. Need to pop out for a school play? Go for it. Fancy working from a coffee shop or even another country? As long as you’re connected, we’re cool with it. And how about a four-day workweek? Yep, we do that too—because we trust our team to deliver, and they do.
But don’t just take our word for it—our Employer Net Promoter Score (NPS) is an impressive 76, and 95% of our team say they’re proud to work at Accurise. That’s the kind of environment where people thrive, and it shows in the work they do.
The Power of a Happy Team
Let’s face it, running a business in the middle of a global pandemic wasn’t exactly easy. But our team stepped up, and then some. The way they adapted, supported each other, and kept everything running smoothly was nothing short of incredible. It’s a testament to the culture we’ve all built together—a culture of trust, respect, and a good dose of fun.
Our approach isn’t just good for our team; it’s also great for our partners. We’re proud to say that we have a 100% client retention rate, and on average, our clients see close to 40% growth in their business after partnering with us. It’s clear that when our team is happy, our partners benefit too.
Why It Matters
In the end, our secret sauce isn’t really a secret at all. It’s just common sense: take care of your people, and they’ll take care of the business. We’re not saying we’ve got it all figured out (seriously, who does?), but we’re committed to learning and growing together.
So, if you’re thinking about how to make your business thrive, start with your team. Create a culture where people love what they do, and everything else will fall into place.
Thanks for being part of our journey—we’re excited about what’s next!
Cheers,
Rob & Jonna