AI and Machine Learning for E-Commerce Risk Management
Description
This book presents a practical, end-to-end approach to risk management in modern e-commerce platforms using machine learning. It covers buyer abuse, seller governance, collusion networks, and payment and credit risk, showing how behavioural, transactional, and network data are translated into actionable risk signals. The book discusses a range of modelling techniques-including tree-based ensembles, sequence and transformer models, graph neural networks, and anomaly detection-focusing on their application under real-world constraints such as class imbalance, latency, adversarial drift, fairness, and regulatory requirements. Extending beyond model development, it addresses deployment, monitoring, governance, and operational assurance, making it relevant to industry practitioners and suitable for advanced courses in applied, industry-focused machine learning.
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Person
Dr Simon Liu is a senior leader in data science and risk management, with experience spanning academia, financial services, and large-scale e-commerce plat forms. He is currently Chief Data and AI Officer at TrustDecision, where he leads the company's data and artificial intelligence strategy in decision intelligence and risk analytics. Dr Liu is also an Adjunct Associate Professor at Nanyang Technological University (NTU), teaching graduate-level courses in natural language processing and ensemble learning, and is coauthor of the textbook Analytic Learning Methods for Pattern Recognition. Previously, he served as Senior Vice President and Head of Data Science for Risk and Security at Lazada and earlier held leadership roles at Scotiabank, where he was Director of AML Modelling and Analytics and led the development of Canada's first AI-powered human trafficking detection model. He is a strong advocate of academic-industry collaboration, focusing on translating advanced machine learning research into practical, production-grade risk systems.
Content
The E-Commerce Risk Landscape.- Data Foundations for E-Commerce.- Tree-based Methods.- Deep Learning Models.- Unsupervised & Anomaly Detection Methods.- Buyer Journey Models.- Buyer-Seller Collusion Models.