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@article{203365,
        author = {Brahmam Odugu and Mahi Jain Roshan and Motwani Gaurav Dilip and Chavan Adityaraj Anirudh and Mansha Jain Roshan},
        title = {Artificial Intelligence and Economic Transformation Through Mathematical Modeling},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11688-11699},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203365},
        abstract = {The convergence of artificial intelligence and mathematical modeling is fundamentally reconstituting the architecture of global economic analysis, forecasting, and decision-making. This paper investigates how AI algorithms grounded in linear algebra, calculus, probability theory, and statistical inference are displacing traditional econometric methods and enabling new forms of economic prediction, optimization, and policy design. We examine the mathematical foundations underlying machine learning models used in economic applications — including gradient descent, matrix factorization, Bayesian inference, and regression analysis — and trace their implementation across a range of high-impact economic domains: stock market prediction, banking fraud detection, consumer behavior analysis, pandemic-era economic forecasting, and GDP modeling. Drawing on published research from economics, data science, and artificial intelligence, the paper develops a five-stage Conceptual Framework linking mathematical foundations through AI algorithm construction and economic data analysis to prediction and strategic decision-making. A comparative analysis of traditional versus AI-based economic modeling is presented in tabular form, and four extended case studies illustrate the practical application of these frameworks in real institutional contexts. The paper further examines the ethical dimensions of AI-driven economic analysis, including issues of algorithmic bias, data privacy, and the concentration of predictive power, and identifies directions for future research. This work is addressed to Class 12 advanced students and young researchers engaged in the intersection of mathematics, artificial intelligence, and economic science.},
        keywords = {Artificial intelligence; mathematical modeling; economic forecasting; machine learning; gradient descent; regression analysis; Bayesian inference; stock market prediction; fraud detection; GDP analysis; predictive analytics; algorithmic economics; data-driven decision making; neural networks; financial mathematics},
        month = {May},
        }