| 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 | 232 | 2015 |
| 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 | 162 | 2015 |
| 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 | 91 | 2017 |
| 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 | 29 | 2016 |
| Using social network knowledge for detecting spider constructions in social security fraud V Van Vlasselaer, J Meskens, D Van Dromme, B Baesens 2013 IEEE/ACM International Conference on Advances in Social Networks …, 2013 | 18 | 2013 |
| 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 | 15 | 2015 |
| 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 | 14 | 2015 |
| Let's talk 3-CD3 L Jones Cambridge University, 2005 | 8 | 2005 |
| Social network analysis for fraud detection B Baesens, V Van Vlasselaer, W Verbeke Fraud Analytics: Using Descriptive, Predictive, and Social Network …, 2015 | 5 | 2015 |
| 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, 35-44, 2015 | 3 | 2015 |
| 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 | | 2018 |
| Effects of community-based churn detection in the telecom sector M Oskarsdottir, J Vanthienen, B Baesens, V Van Vlasselaer, A Backiel European Conference on Operational Research, Date: 2015/07/12-2015/07/15 …, 2015 | | 2015 |
| FAIR: Forecasting and network analytics for collection risk management. V Van Vlasselaer | | 2015 |
| 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 | | 2014 |
| Gotch’all! Advanced network analysis for detecting groups of fraud V Van Vlasselaer, L Akoglu, T Eliassi-Rad, M Snoeck, B Baesens PAW (Predictive Analytics World), Date: 2014/10/29-2014/10/30, Location …, 2014 | | 2014 |
| Social network analytics for fraud detection: insights and challenges B Baesens, V Van Vlasselaer SAS Analytics, Date: 2013/10/21-2013/10/22, Location: Orlando, Florida (US), 2013 | | 2013 |
| Improving fraud detection techniques using social network analytics for the Belgian government V Van Vlasselaer, B Baesens PAW (Predictive Analytics World), Date: 2013/10/23-2013/10/24, Location …, 2013 | | 2013 |
| Social network analysis for detecting spider constructions in social security fraud: new insights and challenges V Van Vlasselaer, D Van Dromme, B Baesens European Conference on Operational Research, Date: 2013/07/01-2013/07/04 …, 2013 | | 2013 |
| 6 Advanced Rule-based Learning: Active Learning, Rule Extraction, and Incorporating Domain Knowledge T VERBRAKEN, V VAN VLASSELAER, W VERBEKE, D MARTENS, ... | | |
