Credit Card Fraud Detection Using Machine Learning Techniques  

作　　者：Ananya Sarker Must. Asma Yasmin Md. Atikur Rahman Md. Harun Or Rashid Bristi Rani Roy Ananya Sarker;Must. Asma Yasmin;Md. Atikur Rahman;Md. Harun Or Rashid;Bristi Rani Roy(Department of Computer Science and Engineering, Bangladesh Army University of Engineering & Technology (BAUET), Natore, Bangladesh)

机构地区：[1]Department of Computer Science and Engineering, Bangladesh Army University of Engineering & Technology (BAUET), Natore, Bangladesh

出　　处：《Journal of Computer and Communications》2024年第6期1-11,共11页电脑和通信（英文）

摘　　要：Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.

关 键 词：Support Vector Machine Decision Tree Nave Bayes Random Forest Matthews Correlation 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]