A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection. (1st May 2023)

• Record Type: Journal Article
• Authors: Fanai, Hosein; Abbasimehr, Hossein
• Is Part Of: Expert systems with applications. Volume 217(2023)
• Publication Date: 2023-05-01
• Subjects: Fraud detection -- Representation learning -- Autoencoder -- ANN -- RNN; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2023.119562
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A two-stage framework to detect fraudulent transactions is proposed. The framework incorporates a deep Autoencoder as a representation learning method. The deep Autoencoder improves the performance of the Deep learning classifiers. The results indicate the superiority of deep autoencoder over PCA. Abstract: Due to the growth of e-commerce and online payment methods, the number of fraudulent transactions has increased. Financial institutions with online payment systems must utilize automatic fraud detection systems to reduce losses incurred due to fraudulent activities. The problem of fraud detection is often formulated as a binary classification model that can distinguish fraudulent transactions. Embedding the input data of the fraud dataset into a lower-dimensional representation is crucial to building robust and accurate fraud detection systems. This study proposes a two-stage framework to detect fraudulent transactions that incorporates a deep Autoencoder as a representation learning method, and supervised deep learning techniques. The experimental evaluations revealed that the proposed approach improves the performance of the employed deep learning-based classifiers. Specifically, the utilized deep learning classifiers trained on the transformed data set obtained by the deep Autoencoder significantly outperform their baseline classifiers trained on the original data in terms of all performance measures. Besides, models created using deep Autoencoder outperform those created using the principal component analysis (PCA)-obtained dataset as well as the existing models.