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@article{203936,
        author = {SUJAL  DHAWALE and Pranita Kachare and Abhay Navsare and Soham Kadam and Abhishek Nagare},
        title = {Artificial Intelligence for Personal Financial Management: Techniques and Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1655-1662},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203936},
        abstract = {Managing personal finances has grown considerably more complex in recent years. People now operate across multiple bank accounts, digital wallets, and payment platforms simultaneously, making it difficult to maintain a clear picture of their financial health. This paper examines how Artificial Intelligence and Machine Learning can be applied to build smart Personal Financial Management systems that assist users in tracking, understanding, and planning their spending effectively. We introduce a three-part structural framework that organises these systems into core analytical functions (transaction categorisation, expense prediction, and anomaly detection), data collection methods (receipt scanning and bank APIs), and user-facing components (conversational chatbots and interactive dashboards). Drawing on a review of recent literature, we observe a clear shift in the field from evaluating isolated algorithms toward engineering complete, end-to-end software platforms. We reinforce this observation with a detailed case study of a working prototype built on React 19, Spring Boot 3, and a dedicated Python FastAPI microservice. The paper concludes by examining persistent challenges such as data privacy and model transparency, and points to future directions including large language models and federated learning.},
        keywords = {Anomaly Detection, Artificial Intelligence, Deep Learning, Expense Forecasting, FinTech, Machine Learning, Natural Language Processing, Personal Financial Management, Survey, Transaction Categorization.},
        month = {June},
        }