5 papers
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production
Wissam Siblini, Guillaume Coter, Rémy Fabry +5
The dark face of digital commerce generalization is the increase of fraud attempts. To prevent any type of attacks, state-of-the-art fraud detection systems are now embedding Machi…
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated ev…
Master your Metrics with Calibration
Wissam Siblini, Jordan Fréry, Liyun He-Guelton +2
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).…
Dataset shift quantification for credit card fraud detection
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However purchase behaviour and fraudster strategies may change over ti…
Multiple perspectives HMM-based feature engineering for credit card fraud detection
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated ev…