Identifying Fraudulent Credit Card Transactions Using Ensemble Learning

Abstract

Recognizing fraudulent credit card transactions is one of the main issues facing banking institutions. Since each transaction that completes the authentication procedure must be authorized by financial institutions, a hacker might pose as the actual cardholder and execute a fraudulent transaction. In this paper, we investigated the capacity of ensemble learning methods to identify credit card frauds on two distinct data sets: the Sparkov synthetic dataset and the real dataset of consumers in the European Union. XGBoost models, random forests, and naive Bayes classifiers are applied and assessed on both datasets. Accuracy, precision, recall, and F1 score are used to measure performance. According to the results, most ensemble classifiers perform exceptionally well on the real-world dataset, but significantly poorly on the simulated dataset. This study showed that, unlike in simulated environments, credit card transaction management scripts are quickly learned in deterministic settings. It is discussed that a larger danger of card information leakage results from strict determinism and lack of randomness.

Keywords

• Fintech
• credit card fraud detection
• ensemble learning
• machine learning
• simulated data set
• real-world data set