What is Autoencoder

Internet-based payment methods in recent times are rapidly growing both in developing and developed economies. Credit card-based payment systems are among the prominent cashless payment methods in all economies. Credit card frauds by cyber-criminals are increasing in spite of several precautionary measures. Thus, fraud detection in real-time is a challenging task. Several machine learning tasks have attempted to solve the problem. This chapter proposes a two-step method to detect credit card fraud by coupling the deep learning-machine learning approaches. In the first stage, the dimensionality of the data set is reduced to 50% by a deep auto-encoder. A machine learning classifier classifies the instances in the second stage. Among the machine learning algorithms, the CatBoost and Random Forest achieved better performance. Their performance aligned with the state-of-the-art approaches. The proposed method is robust against the labor-intensive feature selection and imbalanced class problems.