The Convergence of AI, Cybersecurity, E-Commerce, and Supply Chain Management: A study on Intelligent Systems for Digital Risk Mitigation and Operational Efficiency

This study presents a comprehensive literature review examining the convergence of cybersecurity, e-commerce, supply chain management, and consumer engagement in the digital economy. Drawing from ten scholarly sources, it explores key themes such as fraud detection using machine learning, cost behavior in e-commerce firms, the impact of Internet of Things (IoT) technologies in retail, and data science applications in cybersecurity. A central focus is the role of artificial intelligence (AI) and Genetic Algorithms (GA) in enhancing the accuracy of fraud detection systems, as well as the application of collaborative algorithms to identify fake reviews in online platforms. The research also analyzes how digital transformation influences cost structures, consumer trust, and cybersecurity resilience. Findings reveal that AI and machine learning are essential in improving operational efficiency and protecting against evolving cyber threats. However, challenges such as data quality, model interpretability, and standardization persist. The study highlights the need for strategic, data-driven approaches and interdisciplinary collaboration to address these issues. Implications for future research include the development of real-time fraud detection models, ethical AI deployment, and enhanced public-private cybersecurity partnerships. Ultimately, the research underscores the necessity of intelligent systems and adaptive strategies in building secure, efficient, and consumer-centric digital infrastructures in an increasingly interconnected world