Improving Fraud Detection and Concept Drift Adaptation in Credit Card Transactions Using Incremental Gradient Boosting Trees

Due to the increase in the use of credit cards in electronic shopping, card payments for online commerce have rapidly become a popular trend, which also led to the growth in the number of retailers. Because of these various online shopping options, a more frequent variation in the spending behaviors of the customers and purchasing trends of online markets, known as concept drift problem, can be observed over time which also causes an increase in the need for novel fraudulent strategies. This drifting problem may significantly hinder the effective performance of state-of-the-art fraud detection approaches in real credit card transaction data, which also has the imbalanced class distribution problem. In this study, a card-based incremental Gradient Boosting Tree (GBT) is investigated to detect credit card frauds and to adapt in real-time to drifts occurred in online transactions. The card-based incremental learning is achieved in which the transactions of the fraudulent credit cards reported in each day are incrementally learned by the GBT model. Therefore, the card-based incremental GBT model is compared with the regular GBT model, and retraining of a new transaction set formed by combining the previous set and the transactions of the cards reported as fraudulent. The experiments have been carried out on the 4-month real transaction data from December 2019 to March 2020 in which the concept drift problem occurred in December, dramatically affecting the performance of the GBT model. In these experiments, the improvements in the fraud detection performance have been realized in all months, and also the effectiveness of the card-based increment has been verified by comparing it with the transaction-based incremental learning that may cause catastrophic forgetting problem.