International Journal of Multidisciplinary Research and Publications ISSN (Online): 2581-6187 163 Kavitha Reddy Janamolla and Waheeduddin Khadri Syed, “Global Banking Exploring Artificial Intelligence Role in Intelligent Banking to Automate Trading Platform,” International Journal of Multidisciplinary Research and Publications (IJMRAP), Volume 6, Issue 12, pp. 163- 168, 2024. Global Banking Exploring Artificial Intelligence Role in Intelligent Banking to Automate Trading Platform Kavitha Reddy Janamolla 1 , Waheeduddin Khadri Syed 2 University of the Cumberlands, Williamsburg, KY 1,2 Email address: researchstudent8405@gmail.com Abstract—This research paper investigates the revolutionary impact of AI in the global banking industry. particularly focusing on intelligent banking and the automation of trading platforms. It highlights the various applications of AI, including customer service, risk management, fraud detection, credit scoring, algorithmic trading, market analysis, and portfolio management. Additionally, it examines the benefits and challenges associated with AI implementation and discusses the future outlook of AI in the banking industry. Through a comprehensive analysis of current practices and emerging trends, this paper provides insights into how AI is reshaping the financial landscape. Keywords— Artificial Intelligence, AI, Banking Sector, Intelligent Banking, Automation, Trading Platforms, Customer Service, Risk Management, Fraud Detection, Credit Scoring, Algorithmic Trading, Market Analysis, Portfolio Management, Predictive Analytics, Robo- Advisors. I. INTRODUCTION The banking sector is enduring a significant change determined by swift progressions in technology. Among the utmost influential of these advancements is Artificial Intelligence (AI), which is redefining traditional banking practices and introducing new paradigms of efficiency and personalization. The integration of AI technologies in banking operations has not only enhanced customer experience but also revolutionized various backend processes, making banking more secure, reliable, and efficient. The rapid pace of AI advancements is reshaping the banking industry by enhancing operational efficiencies, improving customer experiences, and enabling the development of innovative financial products (Khadri Syed & Janamolla, 2023). Artificial intelligence (AI) technologies, including machine learning, natural language processing, and predictive analytics, are being used in a variety of banking tasks, including risk management, fraud detection, credit scoring, and customer service. This paper seeks to investigate the multifaceted role of AI in the banking segment, converging on intelligent banking and the automation of trading platforms. The application of AI in intelligent banking encompasses several critical areas including customer service, where Chatbots and virtual assistants powered by AI offer 24/7 assistance; risk management and fraud detection, where predictive analytics and ML algorithms detect possible perils and fraudulent activities; and credit scoring and lending, where AI introduces more accurate and inclusive methods of assessing creditworthiness. Additionally, AI is revolutionizing trading platforms through algorithmic trading, high-frequency trading, and quantitative trading, providing deeper market insights and optimized portfolio management. II. BACKGROUND STUDY Since the middle of the 20th century, artificial intelligence (AI) has been a concept. Early research concentrated on creating algorithms that could simulate human intellect. John McCarthy first used the term "Artificial Intelligence" in 1956 at a conference at Dartmouth College, which signaled the start of AI as a recognized field of study (McCarthy et al., 2006). But with the emergence of big data in the twenty-first century, advanced computing power, and sophisticated machine learning techniques, that AI started to gain significant traction across various industries, including banking (Russell & Norvig, 2016). In the banking sector, financial institutions started utilizing simple AI algorithms for fraud detection and credit scoring in the early 2000s, which marked the beginning of AI adoption. These early applications demonstrated the potential of AI to process massive numbers of data swiftly and precisely, paving the way for more advanced AI-driven solutions (Marr, 2018). For instance, the use of neural networks in credit scoring allowed for more nuanced assessments of borrowers' creditworthiness, moving beyond traditional metrics such as credit history and income levels (Thomas, 2000). As AI technology evolved, so did its applications in banking. Today, AI is used not only for basic tasks but also for complex functions such as predictive analytics, algorithmic trading, and personalized customer service. Predictive analytics, powered by AI, allows banks to forecast market trends and customer behaviors, thereby improving decision- making processes (Davenport & Ronanki, 2018). Algorithmic trading utilizes AI to execute trades based on pre-defined strategies and real-time market data, significantly enhancing trading efficiency and accuracy (Aldridge & Krawciw, 2017). Personalized customer service is another area where AI has made substantial inroads, with AI-driven tools providing tailored financial advice and solutions to customers (Wang, Huang, & Wang, 2020). The widespread adoption of AI in banking is driven by the need for banks to remain competitive in a rapidly changing financial landscape. By leveraging AI, banks can offer innovative services, reduce operational costs, and improve decision-making processes (PwC, 2019). The transformative potential of AI in banking is vast, encompassing areas such as customer relationship management, regulatory compliance, and financial crime prevention. As banks continue to invest in