| dc.description.abstract | Credit card fraud has been increasing with the rise
of technology. This has become a major issue for institutions and
individuals, leading to substantial economic losses and diminished
trust in credit card transactions, especially which occurs online.
Due to the rise of fraudsters who use new technology and ways to
do fraudulent activities, traditional fraud detection methods are
less effective. The existing techniques and approaches that have
been implemented have often failed to identify complex patterns
and relationships of fraudulent activities in this evolving world.
The current machine-learning and deep learning studies have
shown potential limitations due to the use of older datasets with
less data variety while current Graph Neural Network (GNN)
models use complex architectures and high number of features.
Neurashield is a novel Edge Graph Convolutional Network (Edge GCN) approach to enhance the credit card fraud detection using
latest dataset and less number of features. NeuraShield shows the
ability to analyse the complex patterns and relationships of data
by representing transactions as a graph, with nodes indicating
cards and merchants while edges representing relationships.
This approach first preprocesses the data, then applies feature
engineering and at last develops a highly accurate GNN model
which was trained for different class ratios using a real-world
mimicking credit card transaction dataset from IBM that has
24,386,900 unique credit card transactions. The results show that
the Edge-GCN model performed better compared to the Multi Layer Perceptron (MLP) model and also showed an increment
of accuracy when the size of the dataset was increased while
addressing class imbalance. The final results were captured as
a testing accuracy of 80.67%, 0.7138 of F1 score, and 0.8691
of ROC AUC. The evaluation shows that NeuraShield has the
potential to detect more complex credit card fraud activity with
a less information, than the traditional methods. | en_US |
