Advances In Neural Network For Credit Card Fraud Detection

Credit card fraud is the most serious form of risk to financial security and it has a far-reaching effect on consumers and users in general; this makes it necessary that modern methods for machine learning like neural networks should be employed in order to improve their capacity and accuracy for detecting frauds. Various popular neural network architectures for fraud detection, including Feedforward Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Autoencoders, and Generative Adversarial Networks (GAN) are investigated in this study.

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Publication Details

Journal
International Journal on Science and Technology
Published
2025-04-03
DOI
https://doi.org/10.71097/ijsat.v16.i2.3236
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
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article

Advances In Neural Network For Credit Card Fraud Detection

Shikha Srivastava, A. C. Singh
International Journal on Science and Technology
Imbalanced Data Classification Techniques
article

Advances In Neural Network For Credit Card Fraud Detection

Shikha Srivastava, A. C. Singh
article en

Abstract

Credit card fraud is the most serious form of risk to financial security and it has a far-reaching effect on consumers and users in general; this makes it necessary that modern methods for machine learning like neural networks should be employed in order to improve their capacity and accuracy for detecting frauds. Various popular neural network architectures for fraud detection, including Feedforward Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Autoencoders, and Generative Adversarial Networks (GAN) are investigated in this study.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 97%
Imbalanced Data Classification Techniques
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