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Found 4 papers, 2 papers with code
Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives
Although early results of adversarial machine learning indicate the huge potential of the approach to specific domains such as image processing, still there is a gap in both the research literature and practice regarding how to generalize adversarial techniques in other domains and applications.
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
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.
Multiple perspectives HMM-based feature engineering for credit card fraud detection
In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud?
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process.
