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

DOI

10.2991/978-94-6463-094-7_40How to use a DOI?

Keywords

Autoencoder; Credit Card Fraud Detection; Reconstruction Error; Dimensionality Reduction

Abstract

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.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Najmi Rosley
AU  - Gee-Kok Tong
AU  - Keng-Hoong Ng
AU  - Suraya Nurain Kalid
AU  - Kok-Chin Khor
PY  - 2022
DA  - 2022/12/27
TI  - Autoencoders with Reconstruction Error and Dimensionality Reduction for Credit Card Fraud Detection
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 503
EP  - 512
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_40
DO  - 10.2991/978-94-6463-094-7_40
ID  - Rosley2022
ER  -