International Journal of All Research Education and Scientific Methods (IJARESM), ISSN: 2455-6211, Volume 13, Issue 1, January-2025, Available online at: www.ijaresm.com Page | 1708 Spend Analyzer AI: A Comprehensive Expense Tracker with Predictive Modelling for Financial Management Sri Sudha Garugu 1 , Gayathri Belide 2 , Rahul Bandari 3 , Kiran Bejjenki 4 1 Assistant Professor, Department of Computer Science & Engineering (AIML), ACE College of Engineering, Ankushapur, Ghatkesar Mandal, Medchal District, Telangana. – 501301, India ------------------------------------------------------------****************------------------------------------------------------------ ABSTRACT Tracking regular expenses is essential for maintaining a budget and ensuring financial health. Many individuals still rely on traditional methods, such as pen-and-system records or spreadsheets, to track their spending. These methods, however, require additional computation and manual effort to organize, analyze, and predict future spending. This System introduces an intelligent expense tracking system—Spend Analyzer AI—designed to automate and enhance financial tracking through predictive modelling. The system leverages machine learning techniques, including time series forecasting models such as ARIMA, regression-based models for expenditure trends, and classification models like Random Forest and Gradient Boosting for categorizing expenses. By utilizing these methods, the system not only records financial transactions accurately but also forecasts future expenses, offering valuable insights for budgeting and financial planning. This system explores the key components of the proposed system, reviews relevant literature on machine learning models applied to financial forecasting, and discusses the challenges and opportunities for future developments. Keywords: budgeting, expense tracker, financial forecasting, predictive modelling. INTRODUCTION The ability to effectively track personal expenses is a critical component of maintaining financial stability. However, despite the wide availability of digital tools, many individuals still rely on manual methods for tracking their spending, such as using pen and system or spreadsheets. These methods are labour-intensive, prone to error, and lack advanced analytical capabilities. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer promising solutions to automate and enhance financial tracking and prediction. This system discusses the development of Spend Analyzer AI, an intelligent expense tracker that utilizes machine learning models to not only record transactions but also predict future spending patterns and provide personalized financial guidance. LITERATURE SURVEY A. Existing Solutions in Expense Tracking Several commercial tools currently exist to help users track their expenses, including Mint, YNAB (You Need A Budget), and PocketGuard. These applications provide basic functionalities for logging expenses and categorizing them. However, their predictive capabilities are limited. While some applications attempt to forecast future spending, they typically rely on simplistic models that do not account for dynamic and complex financial behaviors. This highlights the need for a more advanced approach, such as the Spend Analyzer AI, which integrates predictive modeling to forecast future expenses with higher accuracy. B.Time Series Forecasting for Financial Prediction Time series forecasting models are integral to predicting future financial trends based on historical data. ARIMA (AutoRegressive Integrated Moving Average) is one of the most widely used methods for time series forecasting and has been successfully applied to financial markets, consumer behavior analysis, and expenditure prediction. ARIMA is particularly effective in capturing trends and seasonal patterns in data, making it suitable for predicting spending behaviors over time.[1] However, ARIMA is limited in handling non-linear patterns, which are often present in financial data, necessitating the use of alternative or complementary modeling techniques.