AI-ML based Expense Categorization and Budgeting System for Personalized Financial Management

Abstract

The challenge of maintaining personal finances impacts everyone across various income levels. Traditional budgeting and expense tracking approaches do not offer real-time engagement, flexibility, or tailored suggestions for one’s finances which is needed in today’s fast-paced world. This work presents an Automated Personal Finance Manager (APFM) that relies on Artificial Intelligence (AI) and Machine Learning (ML) to help users make optimal financial decisions with ease. The proposed framework applies AI-enabled predictive analytics, natural language processing (NLP), and Hybrid Machine Learning (HML) to examine financial transactions, classify expenditures, identify anomalies, and offer personalized budgeting strategies. By implementing both supervised and unsupervised learning, the framework discovers spending behaviors, forecasts prospective financial phenomena, and delivers curated recommendations on investments and savings tailored to the individual’s needs. It further incorporates Reinforcement Learning to enhance finance recommendations based on evolving user interactions with the system over time. The APFM’s intelligent chatbot interface is one of its key features, allowing users to engage with the system and obtain live assistance as needed. Furthermore, confidentiality and integrity of user information are preserved through robust security measures such as data encryption and adherence to financial regulatory standards. Performance assessment of the system is conducted using actual financial datasets to evaluate precision in transaction classification, budget planning, and efficiency in financial suggestion provision. The results show that the HML integrated APFM increases financial literacy, decreases unnecessary spending, and encourages better financial practices and improve the recommendation average accuracy around 97%. This solution is a step forward in AI application development in the fintech industry since it can readily be adjusted to different users’ needs and is easily expanded. This study showcases the role of AI and ML in automating financial planning by making it more available and responsive to users.