Expense Sorted

Beyond LLMs: Advanced Bank Transaction Categorization Methods That Save Time

Ever notice how we default to the shiniest new tool?

In 2025, that's large language models. They're everywhere—writing emails, generating images, and yes, categorizing financial data.

But here's what I learned after spending 462 hours over three years categorizing 18,416 personal transactions: not every AI problem needs a sledgehammer.

The "LLM for Everything" Trap

When I first tackled automatic transaction categorization, I immediately thought: "Let's throw ChatGPT at this!"

Three months and countless frustrations later, I realized I'd fallen into the classic "hammer looking for a nail" trap.

LLMs are magnificent general-purpose tools—they write poetry, explain quantum physics, and generate endless creative variations. But transaction categorization doesn't need creative variations. It needs:

  1. Precision
  2. Consistency
  3. Personalization
  4. Efficiency
  5. Privacy

Most importantly, it needs to understand YOUR specific financial patterns—not generic financial concepts.

The Time Tax of Inaccurate Categorization

Let's talk about the real-world impact of using the wrong tool:

Aspect Using Generic LLMs Using Specialized Model
Accuracy 75-85% 92-98%
Monthly correction time 45-60 minutes 5-15 minutes
Annual time cost 9-12 hours 1-3 hours
Value of time (at $100/hr) $900-$1,200 $100-$300

Those 8-9 hours saved annually might not sound dramatic until you consider: what meaningful project could you complete with an extra full workday each year?

Why Sentence Transformers Outperform LLMs for Your Money

After testing both approaches extensively on my own transactions, the results weren't even close:

Approach #1: Large Language Models

  • Had to send my data to external API
  • Produced creative but inconsistent categorizations
  • Generated plausible-sounding but incorrect categorizations
  • Required significant prompt engineering to get decent results
  • Struggled with my personal merchant naming conventions

Approach #2: Sentence Transformers + Cosine Similarity

  • Runs entirely on my own system
  • Learns my exact personal categorization patterns
  • No "hallucinations" or creative interpretations
  • Directly compares new transactions to my historical decisions
  • Improves precisely with my feedback

The sentence transformer approach converted my transaction descriptions into numerical representations (embeddings) that capture semantic meaning. Each new transaction is compared mathematically to my historical patterns, finding the closest match without guesswork.

The Two-Phase Approach That Transformed My Financial Life

Here's the system I built after trying literally everything else:

Phase 1: Training

  • Feed the model all transactions I've already categorized
  • Convert each description into embedding vectors
  • Create a personalized "financial fingerprint" of my spending patterns
  • Time required: 2-3 minutes, even with thousands of transactions

Phase 2: Categorization

  • For each new uncategorized transaction:
    • Convert to embedding
    • Find closest matches in my historical data
    • Apply my previous categorization decisions
    • Flag truly unique transactions for manual review
  • Time required: seconds for hundreds of transactions

This approach gave me back approximately 42 hours annually—an entire work week I previously spent manually sorting transactions or fixing LLM mistakes.

Beyond Accuracy: The Privacy Factor

Perhaps the most important distinction: My financial data never leaves my system.

With LLMs, your transaction data typically needs to be sent to external APIs—potentially exposing your spending habits, merchant relationships, and financial patterns to third parties.

The sentence transformer approach keeps everything local. Your financial data stays in your Google Sheet or local application—no one else sees where you shop, how much you spend, or what you buy.

Is that privacy worth the 5-minute setup time? For me, absolutely.

The Hybrid Approach: Getting the Best of Both Worlds

After extensive testing, I've found the optimal approach combines both technologies:

  1. Primary Categorization Engine: Sentence transformers handle 90-95% of transactions with high confidence

  2. Fallback Creative Layer: For truly novel transactions with no similar patterns, an LLM can suggest categories based on broader understanding

  3. Human Oversight: Quick reviews of unusual transactions, teaching the system your preferences

This gives you automation without sacrificing accuracy—and more importantly, it gives you back hours of your life previously spent on financial busywork.

Real Results: My Time Freedom Calculation

When I implemented this approach:

Before:

  • 1.5 hours weekly on transaction sorting
  • 78 hours annually (nearly 2 full work weeks)
  • Accuracy around 90% (still required manual corrections)

After:

  • 5 minutes weekly verification
  • 4.3 hours annually
  • Accuracy over 97%

That's 73.7 hours—nearly two full work weeks—reclaimed annually for activities that actually create value in my life and business.

What would you do with two extra work weeks each year?

The Time-Money-Accuracy Triangle

Most solutions force you to pick two:

  • Fast and accurate, but expensive
  • Accurate and affordable, but time-consuming
  • Fast and affordable, but inaccurate

The sentence transformer approach gives you all three by focusing specifically on the transaction categorization problem rather than using a general-purpose AI.

Practical Implementation for Lunch Money Users

If you're using Lunch Money, this approach fits perfectly with your existing workflow:

  1. Training: Train your custom model with all your already-reviewed transactions. This creates your personalized categorization fingerprint.

  2. Categorization: Apply your model to all uncategorized transactions. The system suggests categories based on your historical patterns.

  3. Continuous Improvement: As you review and correct any mismatches, periodically retrain your model to incorporate these new decisions.

The system learns specifically from YOUR categorization choices—not generic financial rules or someone else's spending patterns.

The Calendar Square Calculation

Look at your calendar for the past month. How many squares contain time spent organizing financial data?

Now imagine those squares freed up for:

  • Strategic business planning
  • Time with family and friends
  • That side project you've been meaning to start
  • Simply having space to think and breathe

This isn't just about more efficient transaction categorization—it's about reclaiming irreplaceable squares on your calendar for things that actually matter.

Beyond the Technological: A Philosophy of Time

The tools we choose reflect our values. When I choose a specialized, efficient approach over a flashier but less effective one, I'm making a statement:

My time is too valuable to waste on solutions that prioritize novelty over effectiveness.

The sentence transformer approach isn't the trendiest AI solution for transaction categorization—but it's the one that gives me back the most time with the least ongoing effort.

And ultimately, isn't that the point of financial automation? Not to have the cleverest technology, but to have the technology that most effectively buys back your time?

Your financial life shouldn't demand hours of your attention. It should run smoothly in the background while you focus on living.

Looking for even more advanced financial tracking? Check out our automated expense categorization app that works alongside your Google Sheets for the best of both worlds—privacy and automation.

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