SMARTEXPENSE: A CNN-ENHANCED PERSONAL FINANCE TRACKER WITH ANOMALY DETECTION
DOI:
https://doi.org/10.29121/granthaalayah.v12.i1.2024.6112Keywords:
Cnn, Personal, Finance, Anomaly, Detection, Tracker, SystemAbstract [English]
This project presents an intelligent Expense Tracker system enhanced with anomaly detection capabilities using Convolutional Neural Networks (CNNs). Designed to assist users in effectively managing their personal finances, the system tracks income, expenses, and available balance through an interactive and user-friendly interface. The core interface features include real-time budget summaries, categorized transaction input forms, a transaction history display, and dynamic visual analytics using Chart.js.
Beyond basic tracking functionality, the system integrates a CNN-based anomaly detection module trained to identify irregular or suspicious financial activity based on historical spending patterns. By analyzing temporal and categorical features of transaction data, the CNN model detects anomalies such as unusual spending spikes, duplicate entries, or category mismatches. This feature significantly enhances financial security and promotes better budgeting behavior.
The inclusion of CNNs allows for high-accuracy pattern recognition, offering users intelligent alerts and insights into potential financial inconsistencies. With its seamless blend of financial management tools and AI-driven anomaly detection, the proposed Expense Tracker provides a comprehensive, secure, and adaptive solution for modern personal finance management.
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Copyright (c) 2024 Gargi Chauhan, Yash Chaprana, Divyansh Singh, Dr. Vikesh Kumar
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