Credit Card Fraud Detection Model-based Machine Learning Algorithms.

Fraud detection plays a crucial role in the modern banking sector, aiming to mitigate financial losses affecting both individuals and financial institutions. With a significant portion of the population regularly using credit cards, efforts to enhance financial inclusivity have led to increased card usage. Additionally, the rise of e-commerce has brought about a surge in credit card fraud incidents. Unfortunately, traditional statistical methods used for identifying credit card fraud are time-consuming and may not provide accurate results. As a result, machine learning algorithms have become widely adopted for effective credit card fraud detection. This study addresses the challenge of an imbalanced credit card dataset by employing three sampling strategies: cluster centroid-based majority under-sampling technique (CCMUT), synthetic minority oversampling technique (SMOTE), and oversampling technique. The training dataset is then used to train nine machine learning algorithms, including Random Forest (RF), k nearest neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Ada-boost, Extra-trees, MLP classifier, Naive Bayes, and Gradient Boosting Classifier. The performance of these approaches is assessed using metrics such as accuracy, precision, recall, f1 score, and f2 score. The dataset used in this study was obtained from the Kaggle data repository. [ABSTRACT FROM AUTHOR]