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Dr. D. Maria Sahaya Diran is an Assistant Professor in the Department of Commerce at Kamaraj College (Autonomous), Tamil Nadu, India. His research focuses on e-commerce, digital marketing, consumer behavior, and business analytics.
Artificial Intelligence (AI) is transforming the retail and e-commerce industries through intelligent automation, personalised customer interactions, predictive analytics, and data-informed business strategies. The growing accessibility of consumer data from online platforms, mobile applications, social media, IoT devices, and digital payment methods has enabled merchants to enhance their understanding of client behaviour, streamline operations, and augment supply chain efficiency. Artificial intelligence technologies, encompassing machine learning, deep learning, natural language processing, computer vision, recommender systems, and generative AI, are revolutionising each phase of the retail value chain, from demand forecasting and inventory management to dynamic pricing and consumer engagement. This chapter examines the ideas, technologies, applications, difficulties, and future trajectories of AI-driven transformation in retail, e-commerce, and consumer analytics, emphasising its contribution to improving operational efficiency, customer satisfaction, and sustainable corporate growth.
The swift digital transformation of banking, financial services, insurance, e-commerce, and FinTech has markedly amplified the quantity and intricacy of financial transactions. Although digital financial ecosystems enhance convenience and operational efficacy, they have simultaneously generated novel avenues for hackers to perpetrate fraud, money laundering, identity theft, insider threats, account takeover, and payment fraud. Conventional rule-based detection systems frequently struggle to recognise complex and adaptive criminal activities because of their dependence on established rules and fixed thresholds. Behavioural Analytics has arisen as a formidable AI-driven methodology that examines user behaviour, transaction trends, device attributes, and contextual data to identify suspicious actions instantaneously. Through the amalgamation of Machine Learning (ML), Deep Learning (DL), Big Data Analytics, Graph Analytics, and Explainable Artificial Intelligence (XAI), behavioural analytics empowers financial entities to preemptively detect irregularities, mitigate fraud-related losses, and enhance adherence to regulations. This chapter delineates the principles, framework, methodology, applications, problems, and prospective research avenues of behavioural analytics for the identification of financial crimes within intelligent financial ecosystems.