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@article{187447,
        author = {Joel Vijay Jinna},
        title = {Predictive Business Intelligence Using Deep Learning Models in Financial Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5622-5623},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187447},
        abstract = {Predictive Business Intelligence (BI) brings together data analytics,
machine learning, and business processes to forecast financial results and help organizations make informed decisions. In the modern business world, accurate financial forecasting plays an important role in investment planning, risk control, and operational performance.
This study focuses on the use of deep learning models, especially Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN), in BI systems to improve prediction accuracy. These models can study long- term relationships and complex patterns in time-series financial data,
which traditional statistical techniques often fail to capture.
The paper explains how data is collected and prepared, how models are trained and tested, and how predictive outputs can be used within BI dashboards. The results show that deep learning–based BI systems improve trend prediction, portfolio management, and business decisions.},
        keywords = {Predictive BI, Deep Learning, Financial Forecasting, LSTM, RNN, Business Analytics, Machine Learning, Data Science, Artificial Intelligence},
        month = {November},
        }