no code implementations • 20 Jul 2021 • Wissam Siblini, Guillaume Coter, Rémy Fabry, Liyun He-Guelton, Frédéric Oblé, Bertrand Lebichot, Yann-Aël Le Borgne, Gianluca Bontempi
The dark face of digital commerce generalization is the increase of fraud attempts.
1 code implementation • 6 Sep 2019 • Wissam Siblini, Jordan Fréry, Liyun He-Guelton, Frédéric Oblé, Yi-Qing Wang
Machine learning models deployed in real-world applications are often evaluated with precision-based metrics such as F1-score or AUC-PR (Area Under the Curve of Precision Recall).
1 code implementation • 3 Sep 2019 • Yvan Lucas, Pierre-Edouard Portier, Léa Laporte, Liyun He-Guelton, Olivier Caelen, Michael Granitzer, Sylvie Calabretto
Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.
no code implementations • 17 Jun 2019 • Yvan Lucas, Pierre-Edouard Portier, Léa Laporte, Sylvie Calabretto, Liyun He-Guelton, Frederic Oblé, Michael Granitzer
This phenomenon is named dataset shift or concept drift in the domain of fraud detection.
1 code implementation • 15 May 2019 • Yvan Lucas, Pierre-Edouard Portier, Léa Laporte, Olivier Caelen, Liyun He-Guelton, Sylvie Calabretto, Michael Granitzer
In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud?