FinSight AI – Personal Finance Tracker with Spending Forecasts
Nitu Alam
FinSight — Personal Finance Tracker with Automated Categorisation and Forecasting
FinSight is a deployed Django web application for recording income and expenses, organising transactions, monitoring monthly budgets, and exploring spending patterns through interactive visualisations.
It combines personalised rule-based transaction categorisation with a statistical spending forecast, showing how backend development and data analysis can work together in a practical product.
Live Demo: https://finsight-ai-web.onrender.com
The application is hosted on Render and may take several seconds to wake after a period of inactivity.
Business problem
Manually categorising transactions and comparing expenses against multiple budgets can become repetitive. FinSight provides one place to:
Record and organise financial transactions
Automatically suggest categories from transaction descriptions
Create personal categorisation rules
Track spending against category budgets
Visualise where money is being spent
Export transaction records for further analysis
Estimate upcoming spending from historical activity
✨ Key Features
Secure user registration, login, logout, and session management
Create, view, update, and delete income and expense transactions
Default keyword-based category suggestions
Personal smart rules that map the user's own keywords to categories
Monthly category budgets and progress indicators
Category-level spending visualisation using Chart.js
CSV export for Excel, Google Sheets, or other analysis tools
Thirty-day spending forecast
Responsive interface for desktop and mobile screens
PostgreSQL-backed deployment on Render
How automated categorisation works
FinSight uses an explainable rule-based categoriser rather than claiming that keyword matching is a machine-learning model.
The categoriser checks:
The user's personalised keyword rules
A fallback dictionary of common transaction descriptions
The user's available categories
For example, a user can map dps fee to Education, and later transactions containing that phrase can receive the corresponding category suggestion.
Forecasting approach
Expense transactions are aggregated into a daily time series with pandas. When at least 30 days of data are available, the application fits a SARIMA model with weekly seasonality and estimates total spending for the next 30 days.
When the history is shorter, the application uses an average-daily-spending projection. The forecast is an experimental planning aid and should not be treated as financial advice or a guaranteed prediction
🛠️ Technology Stack
This project was built using a robust and scalable tech stack:
Backend: Python, Django, Gunicorn
Frontend: HTML5, CSS3, JavaScript (ES6)
Database: PostgreSQL
Data processing: Pandas
Forecasting: statsmodels, SARIMA
Visualization: Chart.js
Deployment: Render, Whitenoise
Project structure
🚀 Local Setup and Installation
To run this project on your local machine, follow these steps:
Clone the Repository:
git clone https://github.com/[YourUsername]/finsight-ai.git cd finsight-ai
Create and Activate a Virtual Environment:
For macOS/Linux
python3 -m venv venv source venv/bin/activate
For Windows
python -m venv venv venv\Scripts\activate
Install Dependencies:
pip install -r requirements.txt
Set Up Environment Variables: Create a file named .env in the project root directory. This file will hold your secret key. Do not commit this file to Git.
.env file
SECRET_KEY='your-super-secret-django-key-here' DEBUG=True
You can generate a new secret key using an online tool or a simple Python script.
Run Database Migrations: This will set up your local db.sqlite3 database with all the necessary tables.
python manage.py migrate
Create a Superuser: This will allow you to access the Django admin panel.
python manage.py createsuperuser
Run the Development Server:
python manage.py runserver
The application will be available at http://127.0.0.1:8000/.
☁️ Deployment
This application is configured for seamless deployment on Render using a render.yaml blueprint file. The deployment process includes:
Provisioning a free-tier PostgreSQL database.
Installing all dependencies.
Collecting static files using WhiteNoise.
Running database migrations.
Starting the application with the Gunicorn production server.
To deploy, simply create a new "Blueprint" service on Render and connect it to your GitHub repository.
🔮 Current limitations
Categorisation is rule-based and does not learn a statistical model from user corrections.
The SARIMA configuration uses predefined parameters rather than per-user model selection and validation.
Automated test coverage should be expanded before production use.
Uploaded bank-statement import is not currently included.
Future improvements
Bulk CSV bank-statement import
Date-range and year-over-year reporting
Categorisation model trained from user corrections
Forecast evaluation and confidence intervals
Expanded automated tests
Author
Developed by CoderNitu.
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Posted Sep 13, 2026
Built a Django finance tracker with smart categorization, category budgets, spending charts, CSV exports and forecasts based on transaction history.
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Timeline
Sep 5, 2025 - Sep 10, 2025
