Uncertainty-aware credit card fraud detection using deep learning
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Engineering Applications of Artificial Intelligence, 2023, 123, pp. 106248
- Issue Date:
- 2023-08
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| 1-s2.0-S0952197623004323-main.pdf | Published version | 773.69 kB |
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Countless research works of deep neural networks DNNs in the task of credit card fraud detection have focused on improving the accuracy of point predictions and mitigating unwanted biases by building different network architectures or learning models Quantifying uncertainty accompanied by point estimation is essential because it mitigates model unfairness and permits practitioners to develop trustworthy systems which abstain from suboptimal decisions due to low confidence Explicitly assessing uncertainties associated with DNNs predictions is critical in real world card fraud detection settings for characteristic reasons including a fraudsters constantly change their strategies and accordingly DNNs encounter observations that are not generated by the same process as the training distribution b owing to the time consuming process very few transactions are timely checked by professional experts to update DNNs Therefore this study proposes three uncertainty quantification UQ techniques named Monte Carlo dropout ensemble and ensemble Monte Carlo dropout for card fraud detection applied on transaction data Moreover to evaluate the predictive uncertainty estimates UQ confusion matrix and several performance metrics are utilized Through experimental results we show that the ensemble is more effective in capturing uncertainty corresponding to generated predictions Additionally we demonstrate that the proposed UQ methods provide extra insight to the point predictions leading to elevate the fraud prevention process
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