Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder

Abstract

Fraudulent activities in financial fields are continuously rising. The fraud patterns tend to vary with time, and no consistency can be observed in this regard. The incorporation of new technology by fraudsters is the reason for the execution of online fraud transactions. Given the volatility of the fraud patterns, a good fraud detection model must be able to evolve and update itself to the changing patterns. Thus we aim in this paper to analyze the fraud cases that are unable to be detected based on supervised learning or previous history, create an Auto-encoder model based on deep learning and Compare and assess the performance of the model based on data from different parts of the world and check for the demographic diversity of fraud patterns thereby inferring that the data from which part of the world the model fits the best. The proposed algorithm, deep learning based on the auto-encoder (AE) network is an unsupervised learning algorithm that utilizes backpropagation by setting the inputs and outputs identical. In this research, the Tensorflow package from Google has been employed to implement AE by using deep learning. The accuracy, precision, recall, F1 score and area under the curve(AUC) are all executed to assess the performance of the model.

Keywords

• credit card
• fraud detection
• unsupervised learning
• ann
• deep learning
• autoencoder
• tensor flow