MONEY MAP-EXPENSE AND BUDGET TRACKER

Abstract

In an era of increasing digital transactions, personal financial management applications have become vital tools for users seeking to monitor, analyse, and optimize their spending habits. However, most existing budget tracking systems rely on static rule-based methods with limited adaptability to user behaviour and contextual variability. This paper introduces Money Map, an intelligent expense and budget tracking platform that integrates machine learning, natural language processing (NLP), and predictive analytics to provide personalized financial insights. The system automatically classifies transactions, forecasts future expenditures, and generates adaptive budgets based on user history and lifestyle indicators. Experimental results on real and synthetic datasets demonstrate that Money Map achieves over 90% transaction categorization accuracy, reduces overspending by 18%, and enhances user engagement through predictive alerts. This work highlights how AI-driven personalization can revolutionize digital budgeting systems while preserving user privacy via federated learning and encryption strategies.