Credit Card Fraud Detection using Machine Learning
Researcher: Seleme Shoky, University of Venda
Supervisor: Dr Farai Mlambo, University of the Witwatersrand, Johannesburg
Each year credit card fraud is growing significantly with the advancements of technology resulting in extreme losses to those aﬀected. We build ML model to detect fraudulent activity in credit card transaction systems. The binary classiﬁers build are Neural Network and Random Forest. We used Random Forest for variable importance. The aim to develop an approach which will detect fraud with high recall score and low number of false positives(Sampling Techniques). We used ROC curves, confusion matrices and precision recall statistics to measure the performance of the models.