مجلة الباحث الاقتصادي - Economics Researcher's Journal -
Volume 12, Numéro 1, Pages 174-189
2025-12-28
Machine Learning And Big Data In The Financial Sector: Applications, Algorithmic Trading Integration, Challenges, And Emerging Opportunities
Authors : Djennad Mbarka .
Abstract
This paper explores the growing role of Machine Learning (ML) and Big Data in the financial sector, focusing on their applications in financial prediction and algorithmic trading. It outlines key ML types and techniques used to improve forecasting, risk management, fraud detection, and investment strategies. The study also examnes Big Data’s characteristics, sources, and impact on financial analytics. Special attention is given to algorithmic trading, its strategies, components, and how ML integration enhances performance through real-time data analysis and automated decision-making. Furthermore, the paper discusses the advantages of ML -such as high accuracy and automation- alongside its challenges, including model interpretability, data quality, regulatory concerns, and ethical implications. Finally, it highlights emerging opportunities in using alternative data and advanced learning methods to create more adaptive and effective trading strategies. The findings underscore the transformative potential of ML and Big Data in reshaping modern financial systems.
Keywords
machine learning ; big data ; algorothmic trading ; trading strategies ; financial markets
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