Amount and Location Based Credit Card Fraud Detection

During the pandemic, online transactions have become a usual activity for any purchase. Use of credit card has been growing rapidly, as a result, the number of frauds regarding the credits cards has increased enormously. This includes fraudulent transactions or transfer of money from our accounts. In our paper, an evaluation of machine learning, artificial intelligence, and deep learning algorithms like CNN, Local Outlier Factor, Decision Tree, Cat Boost, XG Boost, Isolation Forest, ANN, K-Means, Naive Bayes, Ada Boosting, Bagging, Multi-Layer Perceptron, KNN, Logistic Regression, Random Forest, SVM, Gradient Boosting, Over Sampling, SMOTE, Extra tree, have been contemplated for credit card fraud detection. In the previous papers where time parameter is considered during the credit card fraud detection whereas, in our paper, we will be considering other factors like location as well as the amount for transaction done. As a result, a distinction was made among different algorithms and datasets based on the factor accuracy.