Personal Expense Leakage Detection and Budget Optimization using Machine Learning

Abstract

Effective personal money management is increasingly challenged by expense leakage, which refers to the gradual and often unnoticed loss of money through unused subscriptions, repeated small transactions, and irregular spending behavior. Most existing budgeting tools focus on summarizing expenses after they occur and provide limited support for identifying hidden or inefficient spending patterns. The primary aim of this project is to assist individuals in managing their finances more effectively by detecting and reducing unnecessary expenses. This project presents a Personal Expense Leakage Detection and Budget Op-timization system developed using Python and machine learning techniques, employing a dual-layer unsupervised learning framework in which the Isolation Forest algorithm is used to identify abnormal transactions such as unexpected charges and billing inconsistencies, while K-Means clustering groups frequent low-value transactions that may be overlooked individually but have a significant cumulative impact. The system further incorporates features such as identification of unused recurring subscriptions, prediction of end-of-month balance based on current spending trends, analysis of behavioral spending patterns to reduce impulsive purchases, and visualization of the long-term financial impact of small recurring expenses. Experimental eval-uation using synthetic transaction data demonstrates that the proposed system is more effective than traditional rule-based budgeting methods in detecting hidden spending patterns, indicating that the integration of machine learning and behavioral analysis into personal finance tools can significantly improve money management, reduce financial waste, and support long-term financial stability.

Introduction

The text explains a machine learning–based system designed to improve personal financial management by detecting “expense leakage” and optimizing budgets. Expense leakage refers to small, often unnoticed transactions such as recurring payments, subscriptions, and irregular spending that gradually lead to financial loss.

Traditional budgeting tools mainly record and summarize expenses but fail to detect hidden patterns or predict future spending. To overcome this, the proposed system uses machine learning techniques—specifically Isolation Forest for detecting abnormal transactions and K-Means clustering for grouping similar spending behaviors. This helps identify unusual expenses, recurring low-value transactions, and unused subscriptions.

The system processes transaction data through stages like data collection, cleaning, normalization, analysis, and visualization. It also provides features such as spending pattern analysis, subscription tracking, and prediction of end-of-month balances. Results are displayed through charts and dashboards for easy understanding.

Conclusion

This project presented a Personal Expense Leakage Detection and Budget Optimization system that utilizes machine learning tech-niques to analyze financial transaction data. The system effectively detects abnormal transactions using the Isolation Forest algo-rithm and identifies spending patterns through K-Means clustering. It helps uncover hidden expense leakage caused by recurring small transactions and unused subscriptions, which are often overlooked in traditional expense tracking systems. The experimental results indicate that the proposed system provides more meaningful insights compared to conventional methods by combining anomaly detection, clustering, and visualization techniques. The system enables users to better understand their spending behavior and take corrective actions to reduce unnecessary expenses. In future work, the system can be enhanced by incorporating real-time data processing, advanced predictive models, and integration with mobile applications to provide more personalized financial recommendations. Overall, the proposed solution contributes to improved financial awareness, efficient budget management, and long-term financial stability

References

[1] M. Sakthivel, P. Roshini, K. Roja, P. Maha Lakshmi, and V. Keerthi, “Personal Expense Tracker Application, ” International Journal of Research Trends and Innovation (IJRTI), Chennai, India, 2023. [2] K. Soundharya, J. Abdul Baasith, A. Mohammed Ayaan, and R. Elangovan,“Personal Expense Tracker,” Journal of Emerging Technologies and Innovative Research (JETIR)

Copyright

Copyright © 2026 Abiya F R, Parvathy S, Devende M R, Ajmalsha S R, Viji C. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.