Comparative assessment of fraudulent financial transactions using the machine learning algorithms decision tree, logistic regression, naïve bayes, k-nearest neighbor, and random forest

HIGHLIGHTS

• What: The models proposed focused on genuine transactions instead of pattern matching or rule-based detection, which would cause missing occurrences. DATASET SAMPLE Use Chip Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Swipe Transaction Online Transaction Swipe Transaction Swipe Transaction Swipe Transaction B. Performance Evaluation Once the models were trained, their performance was evaluated in the testing dataset. The results of this study show the importance of examining multiple metrics, such as accuracy rate, class precision, and class recall when evaluating machine_learning models.
 



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