Developing a Credit Card Fraud Detection Model using Machine Learning Approaches

The growing application and usage of e-commerce applications have given an exponential rise to the number of online transactions. Though there are several methods for completing online transactions, however, credit cards are most commonly used. The increased number of transactions has given the opportunity to the fraudsters to mislead the customers and make them execute fraudulent transactions. Therefore, there is a need for such a method that can automatically classify detect fraudulent transactions. This research study aims to develop a credit-card fraud detection model that can effectively classify an online transaction as fraudulent or genuine. Three supervised machine learning approaches have been applied to develop a credit-card fraud classifier. These techniques include logistic regression, artificial intelligence and support vector machine. The classification accuracy achieved by all the classifiers is almost similar. This research has used the confusion matrix and area under the curve to demonstrate the score of the different performance measures and evaluate the overall performance of the classifiers. Several performance measures such as accuracy, precision, recall, F1-measure, Matthews correlation coefficient, receiver operating characteristic curve have been computed and analysed to evaluate the performance of the credit-card fraud detection classifiers. The analysis demonstrates that the support vector machine-based classifier outperforms the other classifiers.

@article{Khan2022,
  title     = {Developing a Credit Card Fraud Detection Model using Machine Learning Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Shahnawaz Khan and Abdullah Alourani and Bharavi Mishra and Ashraf Ali and Mustafa Kamal},
  doi       = {10.14569/IJACSA.2022.0130350},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130350}
}