Integrating AI-Driven Techniques in Big Data Analytics: Enhancing Decision-Making in Financial Markets

Abstract

The growing complexity and volume of financial data, driven by globalization and advancements in digital technologies, have significantly transformed decision-making processes in financial markets. This paper explores the integration of Artificial Intelligence (AI)-driven techniques in Big Data Analytics to enhance decision-making capabilities in the financial sector. AI techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), are reshaping the landscape of data analytics by providing more accurate predictions, uncovering market trends, and automating complex trading decisions. The study focuses on three core areas where AI-driven techniques have been effectively applied: predictive analytics, sentiment analysis, and algorithmic trading. Predictive models, such as support vector machines and neural networks, are employed to forecast market trends by analyzing vast amounts of historical and real-time financial data. Sentiment analysis, powered by NLP, is used to assess market sentiment from textual data, such as news articles and social media posts, providing additional context to market movements. Lastly, algorithmic trading utilizes AI algorithms to automate and optimize trading decisions based on predefined criteria, enhancing speed and precision in trade execution. Through a quantitative methodology, historical financial data from major stock exchanges were analyzed using AI models. The results demonstrate that AI-driven models, particularly neural networks and sentiment analysis tools, significantly improve prediction accuracy and market timing compared to traditional methods. The findings suggest that integrating AI into Big Data Analytics can lead to more effective decision-making, allowing financial institutions to better manage risks, seize opportunities, and maintain a competitive edge in increasingly volatile markets. Despite the benefits, the paper also addresses the challenges associated with the adoption of AI in financial markets, including issues related to data privacy, model interpretability, and regulatory compliance. As AI technologies continue to evolve, their role in financial markets will likely expand, but careful consideration of these challenges will be critical for their sustainable implementation.