Credit Card Fraud Detection System using Machine Learning

The global danger posed by credit card fraud to financial institutions and customers makes the development of strong fraud detection systems imperative. In this study, we suggest a novel method for detecting credit card fraud that makes use of Convolutional Neural Networks (CNNs) and synthetic data generation. Inadequate feature representations and unbalanced datasets are common problems for traditional fraud detection algorithms. We use synthetic data generation techniques to build a balanced dataset that accurately represents the intricacies of fraudulent transactions in order to overcome these difficulties.

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Publication Details

Journal
Journal of IoT and Machine Learning.
Published
2024-08-16
DOI
https://doi.org/10.48001/joitml.2024.1219-23
Citations
1
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
0.35
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article

Credit Card Fraud Detection System using Machine Learning

Aarth Dahale, Saurav Naik
1 citations
Journal of IoT and Machine Learning.
Imbalanced Data Classification Techniques
0.35
article

Credit Card Fraud Detection System using Machine Learning

Aarth Dahale, Saurav Naik
article en
1 citations

Abstract

The global danger posed by credit card fraud to financial institutions and customers makes the development of strong fraud detection systems imperative. In this study, we suggest a novel method for detecting credit card fraud that makes use of Convolutional Neural Networks (CNNs) and synthetic data generation. Inadequate feature representations and unbalanced datasets are common problems for traditional fraud detection algorithms. We use synthetic data generation techniques to build a balanced dataset that accurately represents the intricacies of fraudulent transactions in order to overcome these difficulties.

Journal of IoT and Machine Learning.Vol. 1(2)
MIT World Peace University (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 35%
Imbalanced Data Classification Techniques
0.35
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