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Browse public repositories on GitHub that use machine learning, data science, and other techniques to detect credit card fraud. Find code, documents, and examples of various models and methods for fraud prevention and detection.
This project aims to detect credit card fraud using various machine learning techniques. It explores the Credit Card Fraud Detection dataset, handles imbalanced data, trains models, and evaluates their performance. The project also investigates the impact of outlier removal on model accuracy.
1 Ιαν 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 meta-classifiers and meta-learning approaches in handling highly imbalanced credit card fraud data.
13 Αυγ 2021 · The credit limit in credit cards sometimes helps us me making purchases even if we don’t have the amount at that time. but, on the other hand, these features are misused by cyber attackers. To tackle this problem we need a system that can abort the transaction if it finds fishy.
Machine learning for credit card fraud detection (ML for CCFD) has become an active research field. This is illustrated by the remarkable amount of publications on the topic in the last decade.
11 Ιουλ 2024 · Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions. Publisher: IEEE. Cite This. PDF. Ibomoiye Domor Mienye; Nobert Jere. All Authors. 3. Cites in. Papers. 2032. Full. Text Views. Open Access.
Credit card fraud poses a significant threat to the financial sector, resulting in substantial financial losses. This research investigates the application of advanced machine learning techniques to effectively detect fraudulent transactions. By utilizing a publicly available dataset, this study evaluates and compares various algorithms, including Random Forest, Decision Tree, and Artificial ...
