Autoencoders with Reconstruction Error and Dimensionality Reduction for Credit Card Fraud Detection


Rosley, Najmi and Tong, Gee Kok and Ng, Keng Hoong and Kalid, Suraya Nurain and Khor, Kok Chin (2022) Autoencoders with Reconstruction Error and Dimensionality Reduction for Credit Card Fraud Detection. Journal of System and Management Sciences, 12 (6). pp. 70-80. ISSN 1816-6075, 1818-0523

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The increase in credit card transactions has inevitably caused an increase in credit card fraud. A total of 157,688 fraud cases occurred in 2018 worldwide, causing a total loss of $24.26 billion. This paper proposes using two types of autoencoder models to detect credit card fraud. The first type uses reconstruction error to detect anomalies in the data. The model detects fraud by defining a threshold in the reconstruction error to flag the transactions as legitimate or fraud. The second type performs dimensionality reduction to encode the data and removes noises. The encoded data were then used to train three other models: K-nearest neighbours (KNN), logistic regression (LR), and support vector machine (SVM). We then applied these models to a European bank's imbalanced credit card data set. A comparison was made between the two autoencoder types and three baseline models: KNN, LR and SVM. The results showed that both autoencoders gave a good and comparable performance in detecting credit card frauds.

Item Type: Article
Uncontrolled Keywords: Autoencoder, credit card fraud detection, reconstruction error, dimensionality reduction
Subjects: H Social Sciences > HF Commerce > HF5001-6182 Business > HF5601-5689 Accounting. Bookkeeping
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 22 Mar 2023 01:48
Last Modified: 22 Mar 2023 01:48


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