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Found 3 papers, 2 papers with code
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model.
Applications
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected.
Profit Driven Decision Trees for Churn Prediction
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base.
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