UCLA

Fraudulent credit card chargebacks continue to be an ongoing issue in the live event ticket-ing industry. Using past work in the field as a guide, logistic, random forest, and k-nearest neighbor models are trained and evaluated using a Tidymodels framework. To address the imbalanced nature of the data set, upsampling, downsampling, SMOTE, ADASYN, and ROSE resampling techniques were applied to the data set. Findings suggest that past results are consistent in that unsampled random forest models perform best for predicting charge- back fraud. The potential to streamline more machine learning models using a tideymodels framework seems possible and would have potential benefit for company use. Sales Amount associated with the order stands out as an influential variable in predicting chargeback fraud.