Digital frauds get a dramatic increase over the years and lead to considerable losses. Detecting fraudulent attempts is valuable to many industries and especially to the banking and financial sectors. To help in anticipating and accurately identifying whether a transaction is fraudulent, machine learning-based models are the key solution for banking and financial institutions. In this paper, an artificial intelligence-based model was built using deep learning and was trained using stochastic gradient descent and feedforward neural networks. The dropout regularization has been utilized to enhance the generalization capabilities of the digital transaction classification model. Different activation functions were used and explored such as the max-out, the hyperbolic tangent, the rectifier linear unit, and the exponential rectifier linear unit. The impact of the learning rate on the model performance was analyzed. For the evaluation of the model, we did use of different metrics such as the accuracy, the precision, and the recall. The obtained results are promising, and the developed model can be used effectively to defend the banking sector against digital frauds.
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