Credit card fraud detection using different classifiers (RF, SVM, Naive Bayes, Logistic Regression, ...) on KNIME platform.
In the financial field, one of the most important aspects is the detection of fraudulent transactions on credit cards. From an economic point of view, it is essential for financial institutions to be able to detect these transactions in advance. In order to complete this task effectively, one of the objectives to be achieved is to identify the best machine learning technique, and in particular the best classifier, that is able to recognize, and consequently block, these types of transactions. The project focuses on counteracting the negative effects that a dataset with unbalanced classes, as in this case, has on finding and estimating the best classifier for forecasting fraudulent transactions. The performance of the models will be evaluated not only through the usual metrics, but also taking into account the computation costs and the quality of the anthropomorphic services provided to the cardholder.
