الفهرس 
An Efficient Credit Card Fraud Detection ModelOnly 14 pages are availabe for public view
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Abstract
In recent years, the market economic order has been negatively harmed by credit card theft, which
has also damaged stakeholder, financial, and consumer interest. Billions of dollars are lost each
year as a result of card fraud losses. As a result, our Thesis offers a methodology for effectively
tackling fraud card detection.
Recently, the dataset for card fraud transactions has been imbalanced because there are many more
regular transactions than there are fraudulent ones. Before addressing the fraud issue, we must
address the imbalanced data issue, which arose when the cases of one class were vastly
outnumbered by those of the other class. As a result, it is difficult to classify fraud because the
outcome could be biased in favor of the dominant group. To address the issue of imbalanced data,
this thesis first uses various resampling approaches, such as oversampling and hybrid sampling
preprocessing techniques and then addresses the issue of fraud.
This thesis aims to investigate fraud detection in financial services using data analysis techniques
by examining several sampling methods that produce and employ synthetic data to resample the
minority class to address the uneven distribution of non-fraudulent and fraudulent classes in a
dataset on credit card fraud. The goal of the thesis is to evaluate these strategies’ efficacy in the
context of fraud detection, which involves a highly unbalanced dataset.
In this thesis, we present four objectives for dealing with the imbalanced dataset problem. The first
objective establishes that oversampling is preferable to downsampling when dealing with
imbalanced datasets. In the second objective, it is demonstrated that machine learning outperforms
deep learning when dealing with imbalanced datasets. The overfitting problem is addressed in the
third objective, and the classification is improved.
Many traditional classifiers usually fail to produce high classification performance, i.e., the
majority class’s accuracy is usually significantly higher than the minority class’s accuracy. The
classification criterion is often set at 0.5, which is inappropriate for an unbalanced classification.
In this thesis, in the fourth objective, we also address using an effective threshold modification
strategy to handle unbalanced datasets. To further improve classification performance, we extend
the method by applying the oversampling preprocessing technique to the training samples.