AI-Assisted Personal Finance Management System with Real-Time Budget Alerts and Smart Expense Categorization
Keywords:
Artificial Intelligence, Personal Finance, Budget Alerts, Expense Categorization, Behavioral Finance, Predictive Analytics, Machine Learning, Financial Awareness, Real-Time Notifications, User EngagementAbstract
This study presents the design and conceptual development of an AI-assisted Personal Finance Management System (PFMS) that integrates artificial intelligence, machine learning, and behavioral finance principles to enhance individual financial decision-making. The proposed system aims to address limitations of traditional financial tracking tools by incorporating real-time budget alerts, intelligent expense categorization, and predictive analytics to deliver personalized financial insights. By leveraging Natural Language Processing (NLP) and pattern recognition techniques, the system automatically classifies transactions and identifies spending patterns, enabling users to monitor and control their finances more effectively. A key feature of the model is the implementation of behavioral nudges, which provide timely and context-aware recommendations to encourage disciplined financial habits, reduce overspending, and improve savings behavior. The system also emphasizes user engagement through adaptive notifications and interactive feedback mechanisms. Additionally, ethical considerations such as data privacy, user consent, and transparency are addressed through explainable AI frameworks to ensure trust and accountability. The study adopts an exploratory and conceptual research approach, drawing insights from existing literature and FinTech practices to develop a scalable framework. The findings highlight the potential of integrating AI-driven analytics with behavioral interventions to significantly improve financial awareness and management. This research lays the foundation for future empirical validation and supports the broader adoption of intelligent, user-centric financial management systems.
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