Véronique Van Vlasselaer

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a Department of Decision Sciences and Information Management, KU Leuven, Naamsestraat 69, B-3000 Leuven, Belgium b Departamento de Ingeniería Industrial, Universidad de Talca, Curicó, Chile c Fraud Risk Management Analytics, Worldline, Brussels, Belgium d Department of Computer Science, Rutgers University, Piscataway, NJ, USA e Department of Computer(More)
As social networks offer a vast amount of additional information to enrich standard learning algorithms, the most challenging part is extracting relevant information from networked data. Fraudulent behavior is imperceptibly concealed both in local and relational data, making it even harder to define useful input for prediction models. Starting from expert(More)
Given a labeled graph containing fraudulent and legitimate nodes, which nodes group together? How can we use the riskiness of node groups to infer a future label for new members of a group? This paper focuses on social security fraud where companies are linked to the resources they use and share. The primary goal in social security fraud is to detect(More)
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Credit card frauds are increasing with the increase in use of plastic money. These frauds include the transactions done either by stealing the physical card or using card data such as card number, expiry date and pin number. There is a need to recognize customer spending pattern and apply validations for incoming transaction. Suspicious transactions can go(More)
Anomaly detection is one of the major requirements of the current age that witnesses a huge increase in online transactions. Data imbalance also poses a huge challenge in the detection process. This paper presents a hybrid metaheuristic algorithm that performs effective anomaly detection on highly imbalanced data. Particle Swarm Optimization is used as the(More)
Fraud is a social process that occurs over time. We introduce a new approach, called AFRAID, which utilizes active inference to better detect fraud in time-varying social networks. That is, classify nodes as fraudulent vs. non-fraudulent. In active inference on social networks, a set of unlabeled nodes is given to an oracle (in our case one or more fraud(More)
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