262 citations · 377 across the 4 of their papers we have counts for
5 papers
Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
Danele Lunghi, Alkis Simitsis, Olivier Caelen +1
Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent a…
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…
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…
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
Fabirzio Carcillo, Yann-Aël Le Borgne, Olivier Caelen +1
Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they…
SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark
Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne +3
The expansion of the electronic commerce, together with an increasing confidence of customers in electronic payments, makes of fraud detection a critical factor. Detecting frauds i…