COMPARATIVE STUDY OF SUPERVISED MACHINE LEARNING ALGORITHMS FOR CREDIT CARD FRAUD DETECTION
Abstract:
Credit card fraud is a significant concern for financial institutions and cardholders, leading to substantial financial losses and a loss of trust in the banking system. To combat this issue, supervised machine learning algorithms have been widely employed for credit card fraud detection due to their ability to learn patterns and detect anomalies in large-scale datasets. This study aims to compare and evaluate the performance of various supervised machine learning algorithms for credit card fraud detection.
The comparative study involves the implementation and evaluation of several well-known supervised machine learning algorithms, including logistic regression, decision trees, random forests, support vector machines, and neural networks. The algorithms are trained and tested on a comprehensive dataset comprising credit card transactions, which includes both genuine and fraudulent transactions.
Performance evaluation metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) are used to assess the effectiveness of each algorithm in detecting credit card fraud. Additionally, computational efficiency and scalability are considered to evaluate the feasibility of deploying these algorithms in real-time credit card fraud detection systems.
The experimental results reveal that different algorithms exhibit varying levels of performance in credit card fraud detection. While some algorithms may achieve high accuracy and precision, they may suffer from low recall, potentially missing a significant number of fraudulent transactions. Conversely, other algorithms may exhibit high recall but lower precision. The trade-off between precision and recall is a crucial factor in selecting an appropriate algorithm for credit card fraud detection, considering the specific requirements and priorities of the application.
This study provides valuable insights into the strengths and weaknesses of popular supervised machine learning algorithms for credit card fraud detection. The findings can assist financial institutions and researchers in selecting the most suitable algorithm or combination of algorithms based on their specific requirements, data characteristics, and performance objectives. Future research directions are also discussed, including the incorporation of ensemble methods and hybrid models to further enhance the accuracy and efficiency of credit card fraud detection systems.