Company · 11 documents
The study comparatively evaluates machine learning models using different class imbalance handling strategies on a dataset of 25,128 credit card applications.
In 2025, global digital payments value reached $2.0 quadrillion across 3.6 trillion transactions, of which $1 trillion were fraudulent.
The study develops a scalable MLOps framework evaluated on 100,000 transactions sampled from the Credit Card Transactions Fraud Detection Dataset.
The proposed Extreme Gradient Boosting (XGBoost) model achieved a highest accuracy of 97.2% in differentiating legitimate and suspicious banking transactions.
The proposed deep learning ensemble combined with the SMOTE-ENN method achieved a sensitivity of 1.000 and a specificity of 0.997.
By 2020, Mastercard and Visa issued 2,287 million credit cards worldwide, with Mastercard issuing 1156 million and Visa issuing 1131 million cards.
The framework addresses extreme class imbalance in a dataset comprising 6,362,620 transaction records, where only 8,213 records (0.1291%) represent fraudulent activity.
The experimental results show that the proposed K-Means clustering model achieved a classification accuracy of 98.07% and an F1-score of 98.08%.
The dataset used for fraud recognition research contains 50,000 samples, consisting of 47,573 normal transactions, 2,423 fraudulent transactions, and four unlabeled samples.
The proposed DAC-CPPO model achieves an overall accuracy of 97.6%, an MAE of 0.074, and an RMSE of 0.081 for portfolio optimization.
The study analyzed a public historical Kickstarter dataset comprising more than 300,000 projects to develop ex ante predictive models for crowdfunding success.
