AI Expense Tracker

Abstract

The rapid advancement of Artificial Intelligence (AI) has paved the way for intelligent financial management tools, including AI-powered expense trackers. This paper presents the development and evaluation of an AI-based expense tracking system designed to automate and optimize personal and small business financial management. Leveraging machine learning algorithms, natural language processing, and real-time data analytics, the system categorizes expenses, detects anomalies, predicts future spending, and provides actionable insights to users. The research explores the integration of AI with user interfaces to enhance usability and engagement, while also addressing challenges such as data privacy, model interpretability, and integration with existing financial ecosystems. Empirical results from user testing and system performance evaluations demonstrate the system\'s accuracy and efficiency compared to traditional expense tracking methods. The study concludes with recommendations for future enhancements and the broader implications of AI in personal finance management.

Introduction

Managing personal and business finances has become increasingly complex due to digital transactions and subscription services. Traditional expense tracking tools like spreadsheets and basic apps are inefficient and fail to provide meaningful insights, leading to poor budgeting and missed financial opportunities. Artificial Intelligence (AI) offers a promising solution by automating transaction categorization, detecting anomalies, forecasting expenses, and providing personalized recommendations through machine learning, natural language processing, and real-time analytics.

This research develops an AI-powered expense tracker using Agile methodology, incorporating features such as automatic transaction classification, anomaly detection, forecasting, a user-friendly dashboard, and strong privacy protections. The system integrates multiple financial data sources and uses machine learning models trained on transaction data to improve accuracy and adaptability.

While AI enhances financial management by reducing manual effort and offering deeper insights, challenges remain, including data quality issues, model generalization across diverse users, anomaly detection precision, forecasting limitations, privacy concerns, and the risk of overreliance on technology.

Conclusion

Overall, the study demonstrates that combining advanced AI techniques with usability and security considerations can create a scalable, intelligent expense tracking system that empowers users to manage their finances more effectively.

References

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Copyright

Copyright © 2025 Prof. Prince Sahu, Ayush Pradhan, Aditya Prakash Nathan, Deeksha Dewangan, Pushpanjali Bhagat, Vasundhra Thakur. 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.