AI and Machine Learning for E-Commerce Risk Management
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Beschreibung
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.
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.
Comprehensive guide that unites payment fraud, buyer-side exploits, seller governance, and collusion detection Leverages the latest advancements in transformer-based sequence models and graph neural networks for patterns of misuse Designed as both a hands-on handbook for data scientists in e-commerce and a practical course text for AI/ML students
Autor*in
Simon Liu
Themen in »AI and Machine Learning for E-Commerce Risk Management«
E-Commerce Risk Management Predictive Engines Fraud Detection Payment Risk Graph Neural Networks Unsupervised Anomaly Detection High-Throughput Scoring with Tree Ensembles Buyer-Seller Collusion Models