PERSONAL FINANCE TRACKER WITH AI BASED EXPENSE PREDICTION

Abstract

In today’s digital age, individuals perform
numerous financial transactions each month through
mobile wallets, banking applications, and online payment
platforms. Although many existing applications record these
transactions, they often fail to deliver meaningful insights
into user spending behavior or provide predictive assistance
for effective budgeting. This paper proposes an AI-powered
personal finance tracking system that leverages behavioral
analysis and machine learning techniques to forecast future
expenses and identify unusual spending patterns. The system
is implemented using Python and Flask, with a MySQL
database for efficient storage and management of
transaction data. Machine learning models such as Random
Forest and Long Short-Term Memory (LSTM) networks are
utilized to analyze user behavior and predict monthly
expenditure trends.
The experimental results demonstrate that the proposed
system enhances users’ financial awareness and enables
better control over spending habits when compared to
conventional static tracking applications. By providing
intelligent insights and predictive recommendations, the
system supports users in making informed financial
decisions and managing their finances more effectively.