Credit Card Fraud Detection Based on Machine and Deep Learning

Abstract

With the rapid evolution of the technology, the world is turning to use credit cards instead of cash in their daily life, which opens the door to many new ways for fraudulent people to use these cards in a bad way. According to the Nilson report, global card losses are expected to exceed $35 billion by 2020. To ensure the safety of users for these credit cards, the credit card’s provider should provide a service to protect users from any risk they may face. Consequently, we present our approach to predict legitimate or fraud transactions on the IEEE-CIS Fraud Detection dataset provided by Kaggel. Our model is BiLSTM- MaxPooling-BiGRU-MaxPooling which based on bidirectional Long short-term memory (BiLSTM) and bidirectional Gated recurrent unit (BiGRU). We also applied six machine learning classifiers which are: Naïve base, Voting, Ada boosting, Random Forest, Decision Tree, and Logistic Regression. Comparing the results from machine learning classifiers and our model the results show that our model achieved better as we got 91.37% score.