Abstract: In the contemporary financial environment, proficient expense management is crucial for achieving success at both personal and organizational levels. Conventional approaches to expense tracking, which often involve manual data entry or the maintenance of physical records, tend to be labour-intensive, susceptible to inaccuracies, and inadequate in scalability to accommodate the increasing volume of both digital and paper transactions. To overcome these obstacles, automated expense tracking solutions have been developed, utilizing advancements in Artificial Intelligence (AI) and Optical Character Recognition (OCR). These solutions are designed to streamline financial management by facilitating efficient, precise, and real-time tracking of expenses. The implementation of OCR technology allows for the automatic extraction of essential financial information, such as dates, amounts, and vendor identities, from various documents, including both printed and handwritten receipts and invoices. Recent technological advancements, including the integration of Tesseract OCR with Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have markedly improved the accuracy of text recognition, even in the case of low-quality or intricate receipts. Additionally, machine learning algorithms enhance these systems by categorizing expenses, identifying spending trends, and providing predictive analytics, thereby equipping users with the tools necessary for making informed financial choices. Automated expense tracking systems address prevalent challenges in financial management, such as errors in data entry, misclassification of transactions, and delays in the reconciliation process. By offering functionalities like real-time categorization and tailored financial insights, these systems meet the diverse needs of both individuals and organizations. However, challenges persist, including the recognition of poorly printed receipts and the computational requirements associated with training sophisticated AI models. Nonetheless, as AI and OCR technologies continue to advance, these systems are set to revolutionize financial management by minimizing manual labour and enhancing accuracy.

Keywords: Automated Expense Tracking, Optical Character Recognition (OCR), Machine Learning Algorithms, Financial Management, Expense Categorisation, Tesseract OCR, Spending Insights, Neural Networks (CNN, LSTM), Real- Time Expense Monitoring, Data Extraction.

| DOI: 10.17148/IARJSET.2025.12142