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Adaptive real-time machine learning for credit card fraud detection

Partners:

  • Gianluca Bontempi (project coordinator and Phd supervisor) Professor at MLG - Computer Science Department, ULB
  • Olivier Caelen (collaborator and industrial supervisor) Atos Worldline fraud detection team, Phd Computer Science Department, ULB
  • Andrea Dal Pozzolo (researcher) Phd candidate at MLG - Computer Science Department, ULB

Funding: Doctiris, Innoviris, Brussels Region

Duration: 2012-2014, renewable for other two more years.

Project Overview:

Nowadays, enterprises and public institutions have to face a growing presence of frauds and consequently need automatic systems able to support fraud detection and fight. 

These systems are essential since it is not always possible or easy for a human analyst to detect fraudulent patterns in transaction datasets, often characterized by a large number of samples, many dimensions and online update.

Project Objectives:

Design, assess and validate a machine learning frame- work able to calibrate in a automatic, real-time and adaptive manner the ATOS Worldline fraud detection strategy.

The goal is to provide the industrial partner with a set of learning tools to be integrated within the credit card fraud detection process daily run by ATOS Worldline in order to improve its robustness, performance and accuracy. 

 

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