University of Southampton (United Kingdom)
Bart Baesens is an associate professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom), as well as an internationally known data analytics consultant. He is a foremost researcher in the areas of web analytics, customer relationship management, and fraud detection. His findings have been published in well-known international journals including Machine Learning and Management Science. Baesens is also co-author of the book Credit Risk Management: Basic Concepts.
By This Author
Fraud Analytics with SAS®: Special Collection
Foreword by Bart Baesens
Current thinking in fraud detection is moving away from the silo approach and recognizing the need for a more proactive and holistic approach to data and analytics. An isolated event may be flagged as suspicious, but without a complete view of the interplaying relationships, the investigator might ignore it.
SAS software provides many different techniques to monitor in real time and investigate your data, and several groundbreaking papers have been written to demonstrate how to use these techniques. Topics covered illustrate the power of SAS solutions that are available as tools for fraud analytics, highlighting a variety of domains, including money laundering, financial crime, and terrorism.
Profit Driven Business Analytics: A Practitioner's Guide to Transforming Big Data into Added Value
By Wouter Verbeke, PhD, Bart Baesens, and Cristián Danilo Bravo Román
Profit Driven Business Analytics: A Practitioner's Guide to Transforming Big Data into Added Value, by Wouter Verbeke, Bart Baesens, and Cristian Bravo, details a progressive value-centric strategy for using analytics to heighten the accuracy of your business decisions and skyrocket your bottom line. Based on the authorial team’s worldwide consulting experience and high-quality research, this step-by-step guide opens up a road map to handling data, optimizing data analytics for specific companies, and continuously evaluating and improving the entire process.
Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS®
Enables you to exercise proficiency in credit risk management, from applied theory to the latest SAS code; build models from the ground up, as well as validate and stress-test existing models; and access exclusive, online materials and a supportive community on a companion website.
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection
By Bart Baesens, Véronique Van Vlasselaer, and Wouter Verbeke
Analytics in a Big Data World: The Essential Guide to Data Science and its Applications
By Bart Baesens
This accessible resource provides a clear roadmap for organizations that want to use data analytics to their advantage but need a good starting point. It covers the topic of data analytics in easy-to-understand terms without overly stressing the mathematical underpinnings, focusing on the business application instead. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and he uses this experience to bring clarity to a complex topic.
- Marie Gaudard is a consultant specializing in statistical training with the use of JMP. She is currently a statistical writer with the JMP documentation team
- Satish Garla is a former Analytical Consultant in Risk Practice at SAS.
- Sam Gardner is a Senior Research Scientist at Eli Lilly and Company where he is focusing on business analytics and using statistical modeling.