The integration of Big Data Analytics and Artificial Intelligence for enhanced predictive modeling in financial markets
Minh Truong
Ho Chi Minh University, Faculty of Finance and Economics
Linh Nguyen
Hanoi University of Science and Technology, School of Computer Science and Engineering
Keywords: Big Data Analytics, Artificial Intelligence, Predictive Modeling, Financial Markets, Machine Learning, Deep Learning, Data Privacy
Abstract
This research article explores the integration of Big Data Analytics and Artificial Intelligence (AI) in the realm of predictive modeling within financial markets. With an ever-increasing volume of financial data, the application of AI techniques to extract insights and improve predictive accuracy has garnered considerable attention. Our study investigates the benefits and challenges of this integration, emphasizing its impact on predictive model accuracy, adaptability, and risk mitigation. The findings reveal that the fusion of Big Data Analytics and AI yields substantial improvements in predictive models. Machine learning and deep learning algorithms efficiently uncover complex patterns in financial data, resulting in more accurate predictions of market behavior, asset prices, and risk assessments. The real-time processing capabilities of AI further enhance adaptability, allowing financial institutions to make informed decisions in rapidly changing market conditions. However, the responsible deployment of AI in financial markets is not without challenges. Data privacy and security concerns are paramount, necessitating robust measures to ensure compliance with data protection regulations. The 'black box' nature of certain AI models also presents transparency and interpretability issues, which are particularly relevant in the finance sector. Our research concludes that the integration of Big Data Analytics and AI offers a promising avenue for revolutionizing predictive modeling in financial markets. It enhances accuracy, adaptability, and risk management, yet the responsible application of AI remains a critical consideration. We propose recommendations that encompass ethical AI and data governance, interdisciplinary collaboration, regulatory compliance, education and training, and further research into model interpretability and data security.
