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.

翻译：深度神经网络(DNNS)在识别信用卡欺诈的任务中进行的无数研究侧重于通过建立不同的网络结构或学习模式来提高点预测的准确性和减少不必要的偏见,通过建立不同的网络结构或学习模式来量化不确定性和点数估计至关重要,因为它减轻了模型的不公平性,并允许从业人员开发值得信赖的系统,这些系统由于信心低而不能作出不最优化的决定。明确评估与DNNS预测有关的不确定性对于真实世界的卡欺诈检测环境至关重要,其原因包括:(a)欺诈者不断改变其战略,因此,DNNS会遇到与培训分配过程不同、而不是由同一过程产生的观测结果,(b)由于耗费时间的过程,由专业专家及时检查的交易很少能及时更新DNNS。因此,本研究报告提出了三种不确定性量化技术(UQ),即称为蒙特卡洛辍学、entemble、和entemample Monte 卡洛辍学,用于对交易数据进行卡欺诈检测。此外,为了评估预测不确定性预测性估计、UQlobism 矩阵和几个绩效指标,因此,我们通过实验性结果显示,我们提出的预测过程产生了更多的不确定性。