Beyond LLMs: Advanced Bank Transaction Categorization Methods That Save Time

Most expense tracking apps rely on outdated categorization methods that waste your time with constant corrections. While Large Language Models (LLMs) seem like the obvious solution, they're expensive, slow, and send your financial data to third parties.

Here's what actually works: a hybrid approach using sentence transformers, Named Entity Recognition (NER), and cosine similarity that's faster, more accurate, and keeps your data private.

The Problem with Current Categorization Methods

Manual Categorization - You spend 15-20 minutes every week fixing categories. That's over 15 hours per year just sorting transactions.

Rule-Based Systems - Work for obvious patterns like "STARBUCKS → Coffee" but fail on edge cases. What about "SQ *CORNER BAKERY" or "PAYPAL *NETFLIX"?

Basic LLMs - Send your transaction data to OpenAI or Claude, costing $0.02-0.05 per transaction and taking 2-3 seconds each. For 100 monthly transactions, that's $24-60 per year just for categorization.

The Sentence Transformer Solution

Sentence transformers convert transaction descriptions into mathematical vectors that capture semantic meaning. Similar transactions cluster together in this vector space, making categorization both fast and accurate.

How It Works

1. Preprocessing - Clean transaction descriptions by removing merchant codes, standardizing formats
2. Vector Encoding - Convert descriptions to 384-dimensional vectors using models like all-MiniLM-L6-v2
3. Similarity Matching - Compare new transactions against your historical data using cosine similarity
4. Confidence Scoring - Only auto-categorize when similarity exceeds 85% threshold

Technical Implementation

from sentence_transformers import SentenceTransformer
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

# Load pre-trained model (downloads once, runs locally)
model = SentenceTransformer('all-MiniLM-L6-v2')

def categorize_transaction(new_description, historical_data):
    # Encode new transaction
    new_vector = model.encode([new_description])
    
    # Find most similar historical transaction
    similarities = cosine_similarity(new_vector, historical_data['vectors'])
    max_similarity = np.max(similarities)
    
    if max_similarity > 0.85:
        best_match_idx = np.argmax(similarities)
        return historical_data['categories'][best_match_idx], max_similarity
    else:
        return "MANUAL_REVIEW", max_similarity

Enhanced with Named Entity Recognition

NER identifies specific entities within transaction descriptions - merchant names, locations, payment processors. This adds context that pure similarity matching might miss.

Common Financial Entities

• Merchant Names - "WHOLE FOODS", "SHELL", "AMAZON"
• Payment Processors - "SQ *" (Square), "PAYPAL *", "VENMO"
• Location Indicators - "NEW YORK NY", "# 1234" (store numbers)
• Transaction Types - "ATM WITHDRAWAL", "DIRECT DEPOSIT"

When sentence transformers and NER disagree, the system flags for manual review rather than guessing.

Performance Comparison

Method	Accuracy	Speed	Privacy	Monthly Cost (100 txns)
Manual	100%	20 min/week	Perfect	$0
Rules Only	70%	Instant	Perfect	$0
GPT-4	92%	2-3 sec/txn	Poor	$50
Sentence Transformers	89%	0.1 sec/txn	Perfect	$0
ST + NER + LLM Fallback	94%	0.2 sec/txn	Good	$5

The hybrid approach achieves 94% accuracy while processing transactions in 0.2 seconds and keeping 90% of your data completely private.

Real-World Implementation

Expense Sorted uses this exact system to categorize transactions in your Google Sheets. Here's how it works in practice:

Phase 1: Bootstrap Learning

Upload your first bank statement. The system learns from your existing categories, building your personal transaction vocabulary.

Phase 2: Confident Auto-Categorization

Transactions with >85% similarity get categorized automatically. "STARBUCKS #1234" matches your previous "STARBUCKS #5678" coffee purchases.

Phase 3: Smart Fallback

Low-confidence transactions (15-20% of total) get reviewed by a lightweight LLM that only sees anonymized patterns, not your raw data.

Phase 4: Continuous Learning

Each manual correction improves the system. Categorize "TRADER JOE'S" as groceries once, and all future TJ's transactions auto-categorize correctly.

Privacy-First Architecture

Your transaction data never leaves your Google Sheet unless you explicitly request LLM assistance for difficult cases. The sentence transformer model runs locally in your browser, keeping your financial data completely private.

Data Processing Hierarchy:

1. Local Processing (90% of transactions) - Sentence transformers + NER
2. Anonymized Cloud (8% of transactions) - Difficult cases sent without personal details
3. Manual Review (2% of transactions) - Truly ambiguous cases you categorize yourself

Getting Started

The easiest way to experience advanced transaction categorization is through Expense Sorted's Google Sheets integration:

1. Upload Your Bank Statement - CSV files from any bank work
2. Review Initial Categories - Help the system learn your preferences
3. Enable Auto-Categorization - Watch future transactions sort themselves
4. Calculate Your Financial Runway - See exactly how many months of freedom your money can buy

Most users save 80% of their categorization time within the first month, spending 3-4 minutes instead of 15-20 minutes per week on expense tracking.

The Time Freedom Connection

Accurate, automated categorization isn't just about convenience - it's about reclaiming your time. Those 15 hours per year you save on transaction sorting can be spent building the skills, relationships, or side projects that actually move you toward financial independence.

When your expense tracking runs itself, you can focus on the bigger questions: How do I increase my savings rate? Which expenses actually improve my life? How many months of runway do I have right now?

Download our free spreadsheet to calculate your freedom number now

The goal isn't perfect categorization - it's using technology to free up your time for decisions that actually matter.

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Ready to automate your expense tracking? Try Expense Sorted's Google Sheets template with built-in AI categorization. Upload your bank statement and see your financial runway in under 10 minutes.

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