I Automated My Expense Tracking in a Weekend - Here’s How

2026-07-26 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

An individual successfully automated personal expense tracking in a weekend by leveraging bank alert emails as the primary data source. The system utilizes Google Apps Script for backend processing, Google Sheets as a database, and Telegram for user interaction. A key component is Anthropic's Claude Sonnet model, which intelligently categorizes transactions, learning and caching merchant-to-category mappings in a "merchant map" sheet to minimize subsequent AI calls. This setup efficiently extracts transaction details, handles new merchants, manages split bills, and allows manual additions, culminating in a mobile-friendly dashboard that displays spending insights and remaining income. The project highlights how modern AI capabilities significantly reduce the effort required for complex, judgment-heavy automation tasks.

Key takeaway

If you are a software engineer or AI student considering personal automation projects, this demonstrates how LLMs have dramatically lowered the barrier for complex tasks. Re-evaluate your "too much effort" pile; what once required weeks of custom logic, especially for judgment-heavy categorization, can now be a weekend project thanks to accessible AI APIs. You can build sophisticated tools with minimal infrastructure.

Key insights

Automating expense tracking is simplified by using bank emails and AI for smart, cached categorization.

Principles

• Email serves as a reliable, historical data source.
• Split problems into deterministic capture and AI judgment layers.
• Cache AI categorization decisions to reduce cost.

Method

The system reads Gmail via Google Apps Script, extracts transaction data using regex, and uses Claude Sonnet for categorization, caching results in a Google Sheet "merchant map." Telegram handles user input for new merchants or split transactions.

In practice

• Use Google Apps Script for serverless automation.
• Implement a merchant map for AI categorization caching.
• Configure LLMs to output JSON last, tolerating prose.

Topics

• Expense Tracking Automation
• Google Apps Script
• LLM Categorization
• Claude Sonnet
• Personal Finance
• Serverless Automation

Best for: AI Engineer, Software Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.