Detect deception on banking credit card payment system by machine learning classifiers

Md Babul Islam; Khandaker Sajidul Islam; Md Helal Khan; Abdullah MMA Al Omari; Swarna Hasibunnahar

doi:10.1117/12.2655113

28 September 2022 Detect deception on banking credit card payment system by machine learning classifiers

Md Babul Islam, Khandaker Sajidul Islam, Md Helal Khan, Abdullah MMA Al Omari, Swarna Hasibunnahar

Proceedings Volume 12339, Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022); 1233927 (2022) https://doi.org/10.1117/12.2655113
Event: Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022), 2022, Chendu, China

Abstract

Credit cards are now being targeted by fraudsters in a variety of ways. Despite the fact that it is never pleasant, this is something that happens to someone in our everyday life. When cardholders disclose their credit card information to others, this happens. In this work, we have constructed a few machines learning (ML) models using anonymous credit card transaction data. The issue in detecting fraud is that it occurs far less frequently than legal transactions. The purpose of this research is to accurately predict fraud transactions. To detect fraud from a vast unbalanced dataset, we used nine different classifiers (Ridge Classifiers, Stochastic Gradient Descent (SGD), Linear discriminant analysis (LDA), Random Forest, Naive Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and k-Nearest Neighbors (k-NN)). In addition, various classifiers were compared to ROC binary classifications. We have shown which classifiers has the best accuracy.

Citation Download Citation

Md Babul Islam, Khandaker Sajidul Islam, Md Helal Khan, Abdullah MMA Al Omari, and Swarna Hasibunnahar "Detect deception on banking credit card payment system by machine learning classifiers", Proc. SPIE 12339, Second International Conference on Cloud Computing and Mechatronic Engineering (I3CME 2022), 1233927 (28 September 2022); https://doi.org/10.1117/12.2655113

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