Turning Hours of Expense Work into Minutes with LLM Expense Analyzer 

Managing company expenses, although complex, sounds like a pretty straightforward process on paper. For our finance team, however, expense reporting had become a monthly ritual of frustration. 

Sifting through hundreds of SmartCat’s expenses, copying each piece of information from financial statements into spreadsheets, and manually sorting them into categories. 

Instead of accounting, it was a repetitive, time-consuming, error-prone grind that was also blocking smarter financial decisions.

To help them reclaim their time, we built the LLM Expense Analyzer, an AI-powered solution that automates expense categorization and reporting.

More importantly, it turned hours, and sometimes days’ worth of work, into mere minutes,  giving SmartCat’s finance team the chance to focus on strategy instead of spreadsheets.

That’s the focus of this case study.

Problem 

What should have been a smooth, reliable process for SmartCat’s finance team felt more like running an obstacle course every month that drained their focus, delayed insights, and left little energy for the bigger financial picture.

Month after month, they had to go through hundreds of transactions, line by line. Dates, amounts, and descriptions, just to name a few, all copied, pasted, and categorized by hand.

It was slow, thankless work. And it came with consequences:

• Hours or even days lost: Time-consuming admin and data entry work consumed most of their time.
• High risk of errors: With so many manual touchpoints, even a small slip, like a wrong category or transposed number, could ripple through reports.
• Reporting bottlenecks: Leadership had to wait while the books crawled their way to completion.
• Expertise wasted: Financial experts were stuck doing admin instead of focusing on higher-level analysis and decision-making.

There had to be a better, more efficient way to get the expense report done in less time. And there was.

Solution

The team didn’t need another off-the-shelf finance tool. They needed something that understood specific statements at SmartCat, fit their workflow, and did the heavy lifting without the need for constant babysitting.

So, we built the LLM Expense Analyzer, a system designed to take over the tedious manual work, improve accuracy, and free up finance to do stuff that actually moves the needle.

Here’s how it works:

• Automated data extraction: We built an extractor capable of handling all statement formats used within SmartCat. No more copy–paste. 
• LLM-powered categorization: A large language model reviews each transaction and assigns the right category. It even generates subcategories to surface deeper insights.
• Actionably reporting: Once the data is processed, the system compiles a report enriched with useful metrics such as:
  • Total spending
  • Spending by category and subcategory
  • Trends over time

In other words, it transforms raw statements into a clear financial story the team can act on immediately.

What makes this unique is not just the automation, but the intelligence behind it. Instead of generic templates, the LLM analyzer was tailored around SmartCat’s own data, workflows, and reporting needs. 

Results

The impact was felt almost immediately. Instead of slogging through endless statements, the finance team could press a button and let the system do the rest.

The difference showed up in four key ways:

• Time savings: Expense categorization went from a monthly headache to a quick task, cutting hours, or even an entire day of work, down to minutes.
• Higher accuracy: The usual errors in dates, amounts, or categories all but disappeared. Reports became more consistent and trustworthy.
• Efficiency boost: Freed from low-value tasks, finance staff could finally dedicate their energy to analysis, planning, and strategic discussions.
• Deeper insights: Structured reports revealed spending patterns, trends, and subcategories that had been invisible before.

By combining automation with AI-driven categorization, we not only reduced manual effort and errors but also provided our finance team with a workflow that was designed specifically for them. 

Plus, they saw a significant increase in productivity and gained the ability to close their monthly reports with greater confidence in the data. Ultimately, the team was able to step into the role they’d always wanted: decision-makers, not data clerks.

About the Client

The client was SmartCat itself, so the SmartCat Labs team took on the challenge. 

As our internal innovation hub, SmartCat Labs experiments with new technologies, prototypes solutions, and builds tools that solve real business problems for both our teams and clients. 

Technologies Used

Here is a quick breakdown of the technologies we used to build the LLM Expense Analyzer: 

• OpenAI API
• Python
• React JS
• MongoDB
• LLMs

Want our custom LLM and AI-powered systems to make your organization more efficient or to uncover insights hidden in your data? Explore our AI solutions or book a strategy call with us!