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@article{199368,
        author = {Chirag Khatri and Dr. Ritu Gautam},
        title = {Financial AI Advisor Using Generative AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {14157-14162},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199368},
        abstract = {The case study involves the design, development, and testing of a Financial AI Advisor that combines real time market data, sentiment analysis and a generative large language model (LLM) specifically OpenAIs GPT 4 to provide data informed investment recommendations using a conversational interface.e. The system uses LangChain, Alpha Vantage, Yahoo Finance, NewsAPI, FinBERT, VADER, and a Streamlit front end to have interactive visualizations, explainable advice, and personal advice to both new and experienced investors. The experimental outcomes prove less than 3 seconds response times, 85% similarity of sentiment generated signals and benchmark market movements, and a high user satisfaction level on informal usability tests. The paper reveals the practicability of marrying generative AI and live financial streams to democratize advanced advisory services and characterizes difficulties with interpretability, data privacy, and regulatory compliance.},
        keywords = {Financial technology, Generative AI, Large language models, Sentiment analysis, Real time market data, Explainable AI, Human AI interaction.},
        month = {April},
        }