Jan 1, 2019 · Performance of Logistic Regression, K-Nearest Neighbour, and Naïve Bayes are analysed on highly skewed credit card fraud data where Research is carried out on examining …

Apr 12, 2022 · The proposed model outperforms the state-of-the-art machine learning and deep learning algorithms for credit card detection problems. In addition, we have performed …

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. By analyzing transaction …

In this research, we suggest a CatBoost-based system for detecting credit card fraud. The approach seeks to correctly identify fraudulent transactions while minimising false positives to …

This study addresses the challenge of an imbalanced credit card dataset by employing three sampling strategies: cluster centroid-based majority under-sampling technique (CCMUT), …

May 12, 2025 · A machine learning-based system for detecting fraudulent credit card transactions using multiple models and a web interface. - druti2k/fraud_detection ... This system uses …

Nov 18, 2023 · In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are firstly used. Then, hybrid methods which use AdaBoost and majority …

es of fraud from the available data using mathematical algorithms is the best effective option. Fraud detection in credit card is the truly the process of identifying those transactions that are …

13 hours ago · Fraud detection systems (FDS) are machine learning based automated systems used by credit card companies to detect fraud before it happens and therefore prevent it from …

Feb 1, 2024 · By examining the strengths and weaknesses of algorithms such as Random Forest, Support Vector Machine, and Neural Networks, the article aims to provide a holistic view of …