Detection of transportation card fraud by classification method in machine learning

Today, machine learning produces concrete and fast solutions to solving many problems both in the scientific world and in the business world, and contributes to the emergence of new cause-effect relationships. Therefore, in recent years, the use of machine learning has become increasingly widespread and has become a popular science. Today, some developments in payment systems have led to the emergence of new problems and continue to do so. At the beginning of the aforementioned problems is the abuse of smart cards. In this study, the most frequently used classification algorithms in the literature were used by examining the abuses made in the transportation cards, which are described as smart cards, and the classification performances of these algorithms were compared and the importance levels of the variables were evaluated according to the best algorithm. The algorithms used are Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, Naive Bayes, Adaboost, XGBoost, K-Nearest Neighbor and Deep Learning algorithms from Artificial Neural Networks. Accuracy, MCC, F1 criteria and AUC criteria were used to measure the classification performance of these algorithms. Again, in this study, first of all, the distribution of all variables on the data set and the normal distribution of the numerical variables were examined, and the logarithmic transformation was applied to the numerical variables that did not show normal distribution. In the study, Boruta method was preferred among the variable selection methods and all variables were found significant according to this method. Before all variables were included in the model, 10-fold cross-validation was applied on these variables and all variables in the data set were included in all machine learning algorithms that were the subject of the study. As a result of all these issues, it has been observed that the XGBoost algorithm has a higher degree of accuracy than other algorithms. The accuracy of the XGBoost algorithm was 0,881, the MCC value was 0,750, the AUC value was 0,953, and the F1 criterion was 0,875. According to this result, it has been observed that XGBoost algorithm is the most successful classifier in detecting transportation card abuse, since it has the highest accuracy and moderate model success.