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Véronique Van Vlasselaer
Véronique Van Vlasselaer
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APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
V Van Vlasselaer, C Bravo, O Caelen, T Eliassi-Rad, L Akoglu, M Snoeck, ...
Decision support systems 75, 38-48, 2015
4932015
Fraud analytics using descriptive, predictive, and social network techniques: a guide to data science for fraud detection
B Baesens, V Van Vlasselaer, W Verbeke
John Wiley & Sons, 2015
3172015
Gotcha! network-based fraud detection for social security fraud
V Van Vlasselaer, T Eliassi-Rad, L Akoglu, M Snoeck, B Baesens
Management Science 63 (9), 3090-3110, 2017
1872017
Determining the use of data quality metadata (DQM) for decision making purposes and its impact on decision outcomes—An exploratory study
HT Moges, V Van Vlasselaer, W Lemahieu, B Baesens
Decision Support Systems 83, 32-46, 2016
442016
Using social network knowledge for detecting spider constructions in social security fraud
V Van Vlasselaer, J Meskens, D Van Dromme, B Baesens
Proceedings of the 2013 IEEE/ACM International Conference on Advances in …, 2013
282013
Guilt-by-constellation: Fraud detection by suspicious clique memberships
V Van Vlasselaer, L Akoglu, T Eliassi-Rad, M Snoeck, B Baesens
2015 48th Hawaii International Conference on System Sciences, 918-927, 2015
212015
Afraid: fraud detection via active inference in time-evolving social networks
V Van Vlasselaer, T Eliassi-Rad, L Akoglu, M Snoeck, B Baesens
Proceedings of the 2015 IEEE/ACM International Conference on Advances in …, 2015
202015
Reframing talent identification as a status‐organising process: Examining talent hierarchies through data mining
S Nijs, N Dries, V Van Vlasselaer, L Sels
Human Resource Management Journal 32 (1), 169-193, 2022
192022
Social network analysis for fraud detection
B Baesens, V Van Vlasselaer, W Verbeke
Fraud Analytics: Using Descriptive, Predictive, and Social Network …, 2015
62015
The quest for ai: A history of ideas and achievements
NJ Nilsson, Y Hilpisch, M Yao, A Zhou, M Jia, B Baesen, ...
Erişim adresi: http://ai. standford. edu/~ nilsson/(Özgün eser 2009 tarihlidir), 2010
62010
Fraud: Detection, Prevention, and Analytics
B Baesens, VV Vlasselaer, W Verbeke
Fraud Analytics Using Descriptive, Predictive and Social Networks Techniques …, 2015
52015
i Verbeke, W.(2015.)
B Baesens, V Vlasselaer
Fraud Analytics Using Descriptive and Social Network Techniques, 0
5
A models comparison to estimate commuting trips based on mobile phone data
CAR Pinheiro, V Van Vlasselaer, B Baesens, AG Evsukoff, MAHB Silva, ...
Software Engineering in Intelligent Systems: Proceedings of the 4th Computer …, 2015
32015
Predictive analytics for fraud detection
B Baesens, VV Vlasselaer, W Verbeke
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques …, 0
2
Talent Identification and Status: Using Data Mining to Understand Talent Hierarchies in Teams
S Nijs, N Dries, V Van Vlasselaer, L Sels
Academy of Management Proceedings 2018 (1), 10747, 2018
12018
Advanced Rule-based Learning: Active Learning, Rule Extraction, and Incorporating Domain Knowledge
T Verbraken, V Van Vlasselaer, W Verbeke, D Martens, B Baesens
Advanced Database Marketing, 145-163, 2016
12016
An Economic Perspective on Fraud Analytics: Calculating ROI of Fraud Detection Systems
B Baesens, V Van Vlasselaer, W Verbeke
12015
FAIR: Forecasting and network analytics for collection risk management.
V Van Vlasselaer
12015
Finding cliques in large fraudulent networks: theory and insights
V Van Vlasselaer, L Akoglu, T Eliassi-Rad, M Snoeck, B Baesens
Conference of the International Federation of Operational Research Societies …, 2014
12014
Building multi-armed fraud detection systems using descriptive, predictive and social network analytics
B Baesens, V Van Vlasselaer, W Verbeke
2015
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