CREDIT CARD FRAUD DETECTION USING STATE-OF-THE- ART MACHINE LEARNING AND DEEP LEARNING ALGORITHMS

Abstract

People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase
in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant
financial losses for both credit card holders and financial companies. In this research study, the main aim is to detect
such frauds, including the accessibility of public data, high-class imbalance data, the changes in fraud nature, and high
rates of false alarm. The relevant literature presents many machines learning based approaches for credit card
detection, such as Extreme Learning Method, Decision Tree, Random Forest, Support Vector Machine, Logistic
Regression and XG Boost. However, due to low accuracy, there is still a need to apply state of the art deep learning
algorithms to reduce fraud losses. The main focus has been to apply the recent development of deep learning
algorithms for this purpose. Comparative analysis of both machine learning and deep learning algorithms was
performed to find efficient outcomes. The detailed empirical analysis is carried out using the European card benchmark
dataset for fraud detection. A machine learning algorithm was first applied to the dataset, which improved the accuracy
of detection of the frauds to some extent. Later, three architectures based on a convolutional neural network are applied
to improve fraud detection performance. Further addition of layers further increased the accuracy of detection. A
comprehensive empirical analysis has been carried out by applying variations in the number of hidden layers, epochs
and applying the latest models. The evaluation of research work shows the improved results achieved, such as
accuracy, f1-score, precision and AUC Curves having optimized values of 99.9%,85.71%,93%, and 98%, respectively.
The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card
detection problems. In addition, we have performed experiments by balancing the data and applying deep learning
algorithms to minimize the false negative rate. The proposed approaches can be implemented effectively for the real-
world detection of credit card fraud.