M Jyothi Reddy, T Sri Apoorva, K Venkatesh, M Harshika, S Sidhartha 03 09 The Deloitte Expense Analysis and Prediction system is designed to analyze organizational expense data and predict future expenditure using data analytics and machine learning techniques Managing large volumes of employee and business expense records manually can be timeconsuming and may make it difficult to identify spending patterns unusual transactions and future budget requirements The proposed system provides a datadriven approach to improving expense monitoring and financial planning The system collects and processes expenserelated information such as employee details expense categories transaction amounts dates departments locations and payment information The collected data is cleaned and transformed to remove inconsistencies and prepare it for analysis Exploratory data analysis is then performed to identify major spending categories departmental expenses monthly trends and variations in expenditure Machinelearning algorithms such as Linear Regression Decision Tree Random Forest and other suitable prediction models can be applied to historical expense data to forecast future expenditure The prediction results can help organizations estimate upcoming expenses and improve budget planning The system can also identify unusually high or abnormal expenses that may require further review Interactive dashboards charts graphs and reports can be used to present expense trends categorywise spending departmentwise expenditure and predicted future expenses These visualizations allow users and management to understand financial patterns quickly and support informed decisionmaking Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026