AI-DRIVEN DECENTRALIZED FINANCE TRACKING SYSTEM WITH BLOCKCHAIN INTEGRATION

Abstract

In the modern digital economy, efficient financial management demands intelligent tools capable of accurate expense tracking and forecasting. This project
introduces an AI-driven expense tracker that integrates Long Short-Term Memory (LSTM) networks with blockchain technology to deliver predictive analytics and secure
financial management. The LSTM model, a variant of recurrent neural networks (RNNs), effectively captures temporal and sequential dependencies in historical transaction
data to forecast future expenses with high precision. By identifying spending patterns, seasonal variations, and anomalies, the system provides users with actionable insights
for budgeting and financial planning. To ensure security, transparency, and trust, the system incorporates blockchain technology with MetaMask-based authentication. Each
transaction is immutably stored on the blockchain, guaranteeing data integrity and preventing unauthorized alterations. This decentralized architecture safeguards sensitive
financial information while maintaining transparency and accountability. The fusion of deep learning and blockchain ensures not only predictive financial intelligence but
also a tamper-proof record of all transactions. Overall, the proposed system offers a secure, transparent, and intelligent expense management solution that empowers users
with foresight, control, and confidence in their financial planning.