Abstract
Abstract: In an era marked by heightened fraudulent activities targeting credit card transactions, this research delves into the efficacy of advanced machine learning algorithms in combating such threats. The performance of logistic regression, decision trees, and the novel random forest approach in identifying fraudulent transactions is scrutinized. Additionally, behavioral biometrics are incorporated as an innovative authentication factor to enhance fraud detection accuracy. Incorporating behavioral biometrics as an additional authentication factor represents a cutting-edge approach to fraud detection, leveraging the unique behavioral patterns exhibited by individuals during transactional interactions.
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