Credit Card Fraud Detection Comparing Multiple Supervised Learning Algorithms for Optimal Accuracy
Year: 2025
Type: article
Abstract: Supervised machine learning algorithms are widely used for classification problems across various domains. However, selecting the best model requires a thorough evaluation of accuracy, robustness, and generalization ability. This research compares multiple supervised learning techniques using real- world datasets, focusing on evaluation metrics such as accuracy, sensitivity, specificity, and AUC-ROC. The study also considers the risk of overfitting, using cross- validation techniques to strengthen the conclusions. Results indicate that AdaBoost achieves near-perfect accuracy while Stochastic Gradient Descent (SGD) provides a balanced performance and generalisation, making their hybrid or combination a preferable choice for fraud detection.
Authors: Ayan Kumar Mahato, Cezan Mendonca, Harita Jasani, Hariharan B
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409379589