A Comparative Analysis of Fraud Detection from Diverse Datasets to Algorithmic Performance

The growth of e-wallets and the ease with which credit card information may be obtained online have contributed to the rise of credit card fraud in the digital age. In order to detect and prevent fraudulent acts in these areas, advanced and effective fraud detection systems must be created. In this work, we describe the results of a deep dive into the field of fraud detection using electronic money and credit card transactions as case studies. We investigate the most recent advances in deep learning, to tackle the challenging problem of fraud detection in these advanced settings. We conduct comprehensive experiments, utilizing e-wallet and credit card transaction datasets, to experimentally assess the performance of the suggested models. In terms of detecting and preventing fraudulent transactions, the experimental results provide more evidence that deep learning models are superior to more traditional rule-based paradigms. Using carefully constructed confusion matrices and receiver operating characteristic (ROC) curves, we elaborate on the models' ability to accurately discern and appraise the fraudulent undertones permeating the complexities of transactional data, as well as a variety of performance metrics.