Credit Card Fraud Detection Using Ensemble Modeling
Roshani Raut, Amrapali Balu Chandanshive, Pragati Nayabrao Gadkar, Esha Govardhan · 2024
The increasing frequency of credit card transactions in the digital age has coincided with a rise in credit card spam, which is dangerous for both consumers and financial institutions. In response, a strong Credit Card Spam Detection system is suggested in this research study. To improve the precision and effectiveness of spam identification, the model incorporates a variety of classifiers, such as Random Forest, Decision Tree, MLP (Multi-Layer Perceptron) Classifier, Naive Bayes, and SVM (Support Vector Machine), Voting classifier. The study assesses each classifier’s performance using a confusion matrix to give a thorough understanding of how successful they are. By providing financial institutions and researchers with a useful tool in the continuous fight against fraudulent activity in the credit card track, the findings hope to strengthen credit card security.