AN ANALYTICAL COMPARISON OF MACHINE LEARNING MODELS FOR CREDIT CARD ANOMALY DETECTION
Keywords:
Machine Learning (ML), XGBoost, Anomaly Detection, Artificial Intelligence (AI), Random Forest (RF)Abstract
In recent times, a significant increase in the use of credit cards have led to a surge in fraudulent activity. It is because of the extensive use of credit cards that the development of online businesses and the convenience of the electronic payment system have both increased. The incorporation of machine learning (ML) techniques is being implemented on larger scales to detect and prevent fraud. When it comes to the analysis of user data, machine learning algorithms are a critical component. In this research, numerous ML-based algorithms for credit card anomaly detection are presented. Some examples of these approaches are the Decision Tree, Random Forest, XGBoost and MLP. The research utilized European card benchmark datasets to carry out the extensive empirical analysis that is performed to detect anomalies in the data. Additionally, the research has also adopted balancing data techniques in order to minimize the number of false negatives. The proposed system can be deployed robustly for the real-world applications banking industries.
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Copyright (c) 2024 Kaushik Sathupadi , Sandesh Achar (Author)
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