Akinde Abdullah, Hassan Omolola, Samuel Olatunde and Oluwadare Aderibigbe, Austin Peay State University, USA

Integrating Artificial Intelligence (AI) into securities trading software has revolutionized the financial markets by enhancing scalability, efficiency, and optimization. This paper explores the historical evolution and advancements in AI-driven trading systems, emphasizing their impact on global financial markets. The study investigates how machine learning, deep learning, and other AI technologies enable sophisticated trading strategies, improve market liquidity, and reduce transaction costs. It also addresses the challenges AI integration poses, including decision-making opacity, bias, increased market volatility, and data privacy concerns. The paper argues for the need for scalable architectures, optimization algorithms, and transparent governance to mitigate these issues. Additionally, human oversight remains crucial for evaluating AI outputs and maintaining accountability. This research aims to provide a comprehensive analysis of advanced AI systems in securities trading, highlighting their potential to enhance market efficiency and stability.

Artificial Intelligence (AI), Securities Trading, Algorithmic Trading, High-Frequency Trading (HFT), Financial Markets Machine Learning (ML), Blockchain, Predictive Analytics, Market Efficiency, Data Privacy, Governance Frameworks, Quantum Computing, Natural Language Processing (NLP), Financial Technology (FinTech), Risk Management.