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A deep learning based credit card fraud detection using feature engineering: An analytical approach

Ajanthaa Lakkshmanan, Gulshan Soni, Anand Kumar Mishra, Senthil Kumar Arumugam, Amit Kumar Tyagi

Abstract


The new advances in online business and e-installment frameworks have started an expansion in monetary misrepresentation cases, for example, credit card extortion. As a result, it is significant to execute instruments that recognise credit card extortion. Highlights of credit card cheats assume a significant part while AI is utilized for fraud recognition, and they should be picked appropriately. With the revolutionized advancements in technology such as deep learning, comes in handy to showcase its complex way and get accurate detection results. This paper introduces a tailored deep learning approach designed to efficiently detect fraudulent activities, encompassing the crucial phases outlined below: a) Gathering data, which includes approximately 153,685 transaction records from Chinese credit cards. b) Utilization of feature engineering techniques to preprocess the data, including analysing spending patterns and time-related features. c) Extraction of essential features using autoencoders. d) Feature selection employing particle swarm optimization (PSO). e) Detecting fraud through the utilization of recurrent neural networks (RNNs). The experimental evaluation provides evidence that the suggested system outperforms current leading models across multiple performance measures, achieving a 0.97 accuracy, a 0.98 sensitivity, and a 0.98 specificity.


Keywords


artificial intelligence; autoencoder; deep learning; feature engineering; PSO; RNN

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References


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DOI: https://doi.org/10.32629/jai.v7i5.1477

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Copyright (c) 2024 Ajanthaa Lakkshmanan, Gulshan Soni, Anand Kumar Mishra, Senthil Kumar Arumugam, Amit Kumar Tyagi

License URL: https://creativecommons.org/licenses/by-nc/4.0/