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Found 5 papers, 3 papers with code
Transfer Learning for Credit Card Fraud Detection: A Journey from Research to Production
The dark face of digital commerce generalization is the increase of fraud attempts.
Master your Metrics with Calibration
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).
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.
Dataset shift quantification for credit card fraud detection
This phenomenon is named dataset shift or concept drift in the domain of 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?
