Data mining for better fraud detection

This research objective focuses on data mining, which is quickly becoming a popular artificial intelligence technique used by tax administrations for better fraud detection. However, many challenges still exist, from using the wide variety of data: traditional financial data, and more novel data sources such as images and data generated through Internet of Things devices, to the need to data mining techniques tailored to such data, with wide applications, incl. customs fraud, black economy, residence and VAT fraud.

​Fraud detection with behavioural data

• researcher Dieter Brughmans 
• supervisor: prof. David Martens 
• research is funded by the University of Antwerp
• Read more on this topic on the website of Antwerp Tax Academy​
  

Explainable Artificial Intelligence (project 1) 

• researcher: Tom Vermeire 
• supervisor: David Martens 
• research is funded by AXA
• Read more on this topic on the website of Antwerp Tax Academy​
  

Explainable Artificial Intelligence (project 2) 

• researcher: Raphael Mazzine  
• supervisor: David Martens 
• research is funded by the Flemish Government​
• Read more on this topic on the website of Antwerp Tax Academy 

2020 

• Vermeire, Tom and Martens, David, "Explainable Image Classification with Evidence Counterfactual", working paper April 16, 2020
  
• ​Vanhoeyveld Jellis, Martens David, Peeters Bruno, "Value-added tax fraud detection with scalable anomaly detection techniques", in Applied soft computing, 86 (2020), 1-2°​
  

2019

• Vanhoeyveld, Jellis, Martens, David, Peeters, Bruno, "Customs fraud detection : assessing the value of behavioural and high-cardinality data under the imbalanced learning issue", in Pattern analysis and applications, London - New york,  Springer, 2019, 1433-7541 (21 p.)
  
•  Vanhoeyveld, Jellis, "Data mining for tax fraud detection", University of Antwerp, Faculty of Business and Economics, 2019, 281 p. (doctoral thesis)

2018

• Vanhoeyveld, Jellis, Martens, David, "Imbalanced classification in sparse and large behaviour datasets", in Data mining and knowledge discovery, Boston, Mass., 2018, 32:1 (2018), p. 25-82
• Vanhoeyveld, Jelllis, Martens, David, "Towards a scalable anomaly detection with pseudo-optimal hyperparameters", Research paper / University of Antwerp, Faculty of Business and Economics ; 2018-012, 59 p.

2016

•  Van Hoeyveld, Jellis, Martens, David, Peeters, Bruno, "Datamining voor fraudedetectie",  in Cahiers politiestudies / Centrum voor Politiestudies,  Gent, 2016, 39:2(2016), p. 167-211
•  Martens, David, De Cnudde, Sofie, Moeyersoms, Julie, Praet, Stiene, Stankova, Marija, Tobback, Ellen, Vanhoeyveld, Jellis, "Big data in banking", in Bank- en financiewezen / Belgisch Financieel Forum. Studiecentrum voor het Financiewezen; Forum financier belge. Centre d'études financières, Brussel, 2016, p. 117-122
•  Vanhoeyveld, Jellis, Martens, David, Peeters, Bruno, "Datamining voor fraudedetectie", in Criminele organisaties en organisatiecriminaliteit, Serie Cahiers politiestudies / Centrum voor Politiestudies, Antwerpen, Maklu, 2016, p. 167-211