A Comprehensive Machine Learning Framework for Anomaly Detection in Credit Card Transactions

Cybercrimes originate in a variety of forms, and the majority of crimes involve credit cards. Despite various steps taken to prevent credit card fraud, it is crucial to alert customers to unusual attempts at fraudulent transactions. The internet has been largely geared to meet this challenge. Many studies have been published over the years to identify anomalies in credit card transactions, and machine learning (ML) has played a significant role in this. Though various anomaly detection techniques are in place, transaction irregularities remain, especially during banking card transactions. The objective of this proposed work is to bring out an efficient machine learning model for identifying abnormal anomalies in credit card-based transactions by considering the limitations of the existing frameworks. The proposed research employs a ML framework comprising data preprocessing, discovering correlations, outlier removal, feature reduction, and classification with a sampling trade-off. The framework uses classifiers such as logistic regression, kNN, support vector machines, and decision trees. The NearMiss and SMOTE approaches are used to address overfitting and underfitting issues through sampling trade-off, which is the defining feature of this research. Significant improvement was noticed when the machine learning models were evaluated using fresh data after a sampling trade-off.

@article{Jeribi2024,
  title     = {A Comprehensive Machine Learning Framework for Anomaly Detection in Credit Card Transactions},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Fathe Jeribi},
  doi       = {10.14569/IJACSA.2024.0150688},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150688}
}