May 13, 2025 · The goal of this project is to develop a machine learning model that can accurately detect fraudulent credit card transactions using historical data.

Apr 28, 2022 · We examine the combination of the following eight supervised machine learning algorithms: linear regression (LR), support vector machine (SVM), Naïve Bayes (NB), random …

Feb 25, 2022 · Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a …

In this study, we investigated the trend of abnormal transaction detection using payment log analysis and data mining, and summarized the data mining algorithm used for abnormal credit …

Feb 9, 2025 · Credit card fraud poses a significant challenge in financial systems, requiring advanced techniques for accurate detection. This paper proposes a hybrid machine learning …

Jun 6, 2024 · As the most powerful and capable model to date, the Transformer large model exhibits revolutionary identification capabilities, making it particularly suitable for recognizing …

Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) are proposed to investigate how hyperparameter adjustment …

May 12, 2025 · This system uses machine learning algorithms to detect fraudulent credit card transactions in real-time. It implements multiple models (Random Forest, XGBoost, and …

Nov 6, 2024 · Fraud can be detected using a variety of machine learning techniques, including random forest, multilayer perceptrons, Naive Bayes, and logistic regression. This study …

Detecting fraudulent transactions in real-time is a critical challenge due to the imbalanced nature of fraud datasets and the evolving tactics of fraudsters. This paper presents a robust machine …