Built an AI-Powered Personal Finance Tracker in n8n (Telegram + LLM + MongoDB)
Hi everyone,
I wanted to share a project I’ve been building in n8n — an AI-powered personal finance tracker that logs and analyzes expenses from natural language messages.
The idea was to make expense tracking as simple as sending a text message instead of filling out forms or spreadsheets.
What it does
Users can send messages like:
“Lunch at cafe 250”
“Amazon shoes 1200 delivered”
“Paid Rahul 500”
“Salary received 25000”
The system automatically:
- Extracts structured data using an LLM
- Classifies the transaction type (Online / Daily / Income / Expense)
- Normalizes categories
- Stores data in MongoDB
- Sends a confirmation message
- Generates weekly XLSX reports
- Provides simple AI-based insights on spending
Core Features
1) AI Information Extraction
An LLM node extracts:
- Item
- Amount
- Category
- Platform or store
- Payment method
- Notes
- Order status
All from a single natural language message.
2) Transaction Classification
Each message is classified into:
- Online
- Daily (offline)
- Income
- Expense (money sent to someone)
This classification controls routing and storage logic.
3) Category Normalization
I implemented a basic “memory” system:
- Categories are stored in MongoDB
- The AI compares new categories with existing ones
- Similar categories are reused
- New ones are created only when needed
This keeps the data more consistent.
4) Multi-Path Logging
There are different flows for:
- Online purchases
- Daily expenses
- Income
- Peer-to-peer transfers
Each is stored with slightly different fields where appropriate.
5) Weekly Reports
A scheduled workflow:
- Aggregates user data
- Converts it to an XLSX file
- Sends it to the user via Telegram
6) Conversational Finance Assistant
There is also a simple conversational layer where users can ask for:
- Spending summaries
- Basic insights
- Habit-related feedback
This uses short-term memory and stored transaction data.
Tech Stack
- n8n for automation
- Telegram Bot API
- LLM nodes for NLP and classification
- MongoDB for storage
- Redis for session memory
- Airtable for long-term memory
- XLSX generation for reports
Why I built it
Mainly to:
- Learn more about AI + automation design
- Experiment with real-world LLM use cases
- Build something practically useful
It started small and gradually evolved into a more complete system.
Looking for feedback
I’d appreciate suggestions on:
- Optimization
- Data modeling improvements
- Scaling AI-heavy workflows
- Feature ideas
Posted Feb 14, 2026, 7:56 AM UTC