Bart Baesens
Associate Professor
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®
By Bart Baesens, Daniel Roesch and Harald Scheule
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
This book addresses the different types of analytics in detecting fraud, including descriptive analytics, predictive analytics, and social network analysis.
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.
Featured Authors
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Robert N. Rodriguez retired from SAS in 2018 after serving as a senior director in research and development. Bob received a PhD in statistics from the University of North Carolina at Chapel Hill and was a research statistician at General Motors Research Laboratories before joining SAS.
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Ron Klimberg, PhD, is a professor at the Haub School of Business at Saint Joseph's University in Philadelphia, PA.
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Terisa Roberts is a director and Global Solution lead for Risk Modeling and Decisioning at SAS. She has extensive experience in quantitative risk management, regulatory compliance, and model governance and validation. She has worked in financial services, telecommunications, government, energy, and retail sectors.
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Todd Case has been working in the pharmaceutical industry as a statistical programmer for over 20 years. He completed his MS in Epidemiology from Columbia University and has worked at Forest Laboratories (now Allergan), Genentech, Bristol-Myers Squibb, and Biogen. He currently works at Vertex Pharmaceuticals.