AI-Powered Predictive Analytics for Financial Forecasting and Strategic Insight

Umer Mukthar Mohiddin Hasan. and Dr. Shreevamshi N
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Umer Mukthar Mohiddin Hasan.: Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India
Dr. Shreevamshi N: Department of Management Studies, Dayananda Sagar College of Engineering, Bangalore, India

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 6, 532-555

Abstract: The rapid evolution of artificial intelligence (AI) has significantly reshaped the financial services landscape, particularly in the domain of predictive analytics. The integration of machine learning (ML), deep learning (DL), and advanced statistical models has empowered financial institutions to forecast market trends, identify anomalies, and enhance investment strategies with improved precision and agility. This chapter explores the theoretical foundations, algorithmic techniques, and practical applications of AI-driven predictive analytics in financial trend forecasting. It traces the progression from traditional statistical methods to dynamic, real-time AI-powered systems fueled by vast datasets and adaptive algorithms. A spectrum of ML models—including decision trees, support vector machines, and neural networks—are examined alongside ensemble approaches such as Random Forest and XGBoost, and time series-oriented deep learning architectures like LSTM and GRU. The chapter focuses on how diverse financial data—including stock prices, interest rates, macroeconomic indicators, and unstructured sources such as news and social media sentiment—can be integrated to develop robust predictive frameworks. It further investigates the incorporation of economic indicators to strengthen contextual forecasting and improve anticipatory decision-making. Real-world case studies in portfolio management, credit analysis, and algorithmic trading are presented to demonstrate applied relevance. The chapter also addresses technical challenges including data quality, overfitting, feature selection, and the interpretability of complex models, emphasizing the need for explainable AI (XAI) in high-stakes financial environments. Ethical considerations such as algorithmic bias, data privacy, and regulatory compliance are critically discussed to highlight the societal responsibilities of AI implementation in finance. Finally, the chapter explores emerging frontiers such as the fusion of AI with edge computing for real-time prediction, reinforcement learning for adaptive strategy optimization, and quantum computing for enhanced analytical depth. Through a synthesis of conceptual insights and empirical evidence, this chapter aims to enrich scholarly and professional discourse on AI-based predictive analytics in finance.

Date: 2025
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