EXPENSE TRACKER

Abstract

The abstract proposes the development of an Expense Tracker to address the challenges of managing expenses in a rapidly changing economic environment. As financial landscapes evolve, individuals encounter increasing complexity in tracking and managing their expenditures. The proposed system leverages advanced technologies such as machine learning, artificial intelligence, and data analytics to provide a sophisticated, user- centric platform for monitoring, analyzing, and optimizing personal spending habits. One of the core features of the Expense Tracker is real-time transaction tracking. This functionality enables the system to capture and update transactions instantaneously, providing users with an accurate and up-to-date overview of their financial activities. By ensuring that users are always aware of their current financial status, the Expense Tracker empowers them to make informed decisions promptly, thereby enhancing their financial management capabilities. Additionally, the Expense Tracker incorporates automated categorization, a feature that utilizes machine learning algorithms to classify expenses into

predefined and customizable categories. This automation reduces the need for manual input, minimizes errors, and offers a seamless user experience. By improving the accuracy of spending analysis, users can gain a clearer understanding of their financial behaviors and make adjustments as needed. The system also supports customizable budget allocation, allowing users to set and adjust budgets according to their individual needs and financial goals.