A Meta-analysis of Credit Card Fraud Detection Using Machine Learning Techniques

Credit cards play a crucial role in daily life but are also vulnerable to illegal activities. This study leverages data from the studies in the Web of Science database where Machine Learning techniques were utilized for detecting credit card fraud. Each study’s models provided seven key variables, including the algorithm used, methodology, and dataset specifics (such as variable count, sample size, dataset balance, etc.). The primary objective is to uncover factors that impact the effectiveness of credit card fraud detection models. To achieve this, the study employs five linear regression models, using Accuracy, AUC, Precision, Recall, and F1-Score as outcome measures.