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@article{180534,
        author = {Sahil Ramchandra Chavan and Shubham Sachin Bangal and Shubham Sanjay More and Sakshi Balasaheb Autade},
        title = {VISION WEALTH: An AI-Powered Personalized Financial Planning Tool},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2506-2510},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180534},
        abstract = {In today’s dynamic financial landscape, individuals require intelligent and accessible tools to manage their finances effectively. This study introduces Vision Wealth, a personalized financial planning platform that integrates deep learning with rule-based financial logic to deliver tailored, actionable recommendations. Users submit key demographic and financial information through a secure, Flask-based web interface, that supports real-time interaction, data visualization, and PDF report generation. The system employs a hybrid inference engine, comprising a pre-trained neural network and domain-specific financial formulas to predict optimized budget allocations across categories such as savings, investments, insurance, emergency funds, healthcare and retirement planning. Evaluations using simulated and real user inputs demonstrated the platform’s effectiveness in generating realistic and practical financial plans. With its emphasis on personalization, transparency and usability, Vision Wealth showcases the convergence of AI and financial technology in empowering individuals to make informed financial decisions.},
        keywords = {Artificial Intelligence, Budget Allocation, Deep Learning, Financial Planning, Flask, Personal Finance, Rule-Based Systems.},
        month = {June},
        }