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Auto-Encoder and LSTM-Based Credit Card Fraud Detection

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Abstract

The increased fraud risk due to the most recent methods of paying with a credit card, such as real-time payments and cards with near-field communication (NFC) capabilities, makes detecting credit card fraud an essential topic of study. Deep learning has shown encouraging results in recent years, whenever it is used to detect credit card fraud. There have been several deep learning-based models presented in this field of research. Still, not all of them have proven to be the most effective because each technique is best used with a particular dataset, and fraudsters constantly refine their methods to evade detection by the systems in place today. This work's primary focus is detecting credit card fraud using an auto-encoder with GRU and LSTM models. This is an efficient mechanism; first, data are passed to auto-encoder without the labels, and after that, the output generated by the auto-encoder is passed to the LSTM model as input with labels to detect fraud.

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Data availability

Information regarding the dataset which is used for this research is mentioned in the Methodology section. The dataset is publicly available.

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Authors and Affiliations

  1. UIET, MDU, Rohtak, India

    Deepthi Sehrawat

  2. Computer Science and Engineering, UIET, MDU, Rohtak, India

    Yudhvir Singh

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  1. Deepthi Sehrawat
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  2. Yudhvir Singh
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Correspondence to Deepthi Sehrawat.

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This article is part of the topical collection “Machine Intelligence and Smart Systems” guest edited by Manish Gupta and Shikha Agrawal.

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Sehrawat, D., Singh, Y. Auto-Encoder and LSTM-Based Credit Card Fraud Detection. SN COMPUT. SCI. 4, 557 (2023). https://doi.org/10.1007/s42979-023-01977-w

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