Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

About the Authors

Professor Bart Baesens is a professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data and analytics, fraud detection, customer relationship management, web analytics, and credit risk management. His findings have been published in well-known international journals (e.g., Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research) and presented at international top conferences. He is also author of the books Credit Risk Management: Basic Concepts (http://goo.gl/T6FNOn), published by Oxford University Press in 2008; Analytics in a Big Data World (http://goo.gl/k3kBrB), published by John Wiley & Sons in 2014, and Beginning Java Programming: The Object-Oriented Approach (http://goo.gl/qHXmk1), published by John Wiley & Sons in 2015. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises, and provides consulting support to international firms with respect to their big data and analytics strategy.

Véronique Van Vlasselaer graduated magna cum laude as master information systems engineer at the faculty of business and economics, KU Leuven (Belgium). For her master's thesis topic, “Mining Data on Twitter,” she received the best thesis award from the faculty's student branch. In 2012, Véronique started as ...