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FINTRACK-AI: An Automated Finance and Expense Tracker

Dr. Devidas Thosar, Disha Deshmukh, Atharv Deshmukh, Ishwari Mane

Abstract


This project presents the design and development of FinTrack AI, an intelligent expense management system that uses machine learning to automate personal finance tracking through SMS-based UPI transactions. The system reads transactional messages from the user's mobile inbox, extracts critical information such as amount, sender, and transaction type using Natural Language Processing (NLP) and regular expressions, and classifies each entry into categories like food, bills, salary, or shopping.

To achieve this, the system employs a combination of TF-IDF vectorization and Logistic Regression for accurate text classification. Regular expression-based parsing enhances feature extraction, ensuring structured input for the ML pipeline. The model is trained on a labeled dataset of UPI SMS samples, and integrated into a mobile application to display categorized transactions, visual summaries, and trend insights.


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References


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