AI and Machine Learning for E-Commerce Risk Management

Siyu Liu (Autor)

Buch | Hardcover

2026
Springer Verlag, Singapore
978-981-95-6183-4 (ISBN)

Titel nicht im Sortiment

Artikel merken

This book offers a comprehensive guide to leveraging machine learning and predictive analytics for managing the full spectrum of risks inherent in modern e-commerce platforms—from payment fraud and chargebacks to platform-specific abuses like voucher manipulation, collusion rings, and promotional gaming. Grounded in real-world practice, it shows how to transform raw transaction logs, user clickstreams, and promotional datasets into actionable risk scores that power both real-time interdiction and strategic oversight. Readers discover a suite of methods—ranging from gradient-boosted trees and deep sequence models to graph neural networks and unsupervised anomaly detectors—each chosen for its ability to detect subtle, evolving patterns of misuse.

Dr. Simon Liu is an experienced leader in data science and risk management, with a career spanning academia, financial services, and e-commerce. He is currently the chief data and AI officer at TrustDecision, where he oversees the company’s data and artificial intelligence strategy, focusing on innovation in decision intelligence and risk analytics. Dr. Liu also serves as an adjunct associate professor at Nanyang Technological University (NTU), where he teaches graduate-level courses in natural language processing and ensemble learning within the School of Electrical and Electronic Engineering. He supervises research students and is the co-author of the graduate textbook Analytic Learning Methods for Pattern Recognition. Earlier in his career, he held leadership roles at Scotiabank in Canada, culminating as the director in AML modelling and analytics, where he pioneered Canada’s first AI-powered model for human trafficking detection. Dr. Liu remains committed to advancing the integration of academic research and industrial practice.

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.

Erscheint lt. Verlag	19.5.2026
Reihe/Serie	Transactions on Computer Systems and Networks
Zusatzinfo	Approx. 250 p.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
Schlagworte	Buyer-Seller Collusion Models • data engineering • E-Commerce Risk Management • fraud detection • graph neural networks • High-Throughput Scoring with Tree Ensembles • machine learning • Payment Risk • Predictive Engines • Unsupervised Anomaly Detection
ISBN-10	981-95-6183-3 / 9819561833
ISBN-13	978-981-95-6183-4 / 9789819561834
Zustand	Neuware