Artificial Intelligence In E-Commerce: A Review Of Personalization, Dynamic Pricing, Visual Search, Fraud Detection And Agentic Commerce

Artificial intelligence is transforming e-commerce by changing how businesses attract, engage and retain customers. This paper reviews AI applications across the e-commerce value chain. It examines personalized recommendation, from item-to-item collaborative filtering to deep click-through-rate models that capture changing user interests; dynamic and algorithmic pricing and its effects on marketplaces; demand forecasting and price optimization for inventory planning; visual and voice search; conversational chatbots and assistants for customer support; and fraud detection on payments and accounts. Using machine learning, natural language processing and computer vision, these technologies analyse customer behaviour and transactions in real time, increasing conversion, order value and satisfaction while reducing operational cost and losses. The paper also discusses emerging trends such as generative AI for product content, conversational commerce, hyper-personalization and autonomous shopping agents. A five-layer e-commerce AI architecture comprising data, customer intelligence, decision, engagement and trust layers is proposed, along with research gaps, objectives, a comparison of applications and challenges including privacy, algorithmic bias, transparency, implementation cost and skills. The review concludes that long-term success depends on responsible and transparent AI that protects consumer trust.