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A curated list of data mining papers about fraud detection.
Fraud Detection model based on anonymized credit card transactions
Credit card fraud is a significant problem, with billions of dollars lost each year. Machine learning can be used to detect credit card fraud by identifying patterns that are indicative of fraudulent transactions. Credit card fraud refers to the physical loss of a credit card or the loss of sensitive credit card information.
Credit Card Fraud Detection Project with Code and Documents
Credit Card Fraud Detection
Implementation of an intelligence system to detect the fraud cases on the basis of classification.
This is an Online Transaction Fraud Detection System (FDS) to detect payment frauds. Made using Django.
Full Stack Credit Card Fraud Detection Using Machine Learning with Code and Documents Plus Youtube Explanation Video
Comparison b/w Federated Learning & Split Learning for credit card fraud detection dataset using Pytorch
Bank card fraud detection using machine learning. Web application using Streamlit framework
A machine learning system designed to identify fraudulent credit card transactions using Python and statistical analysis.
Credit Card Fraud Detection using Logistic Regression on credit card dataset
Credit Card Fraud Detection using Machine Learning, I have undertaken the task of developing a robust system to identify fraudulent credit card transactions.
This project aims to detect credit card fraud using Anamoly detection techniques such as Isolation Forest and Local Outlier Factor algorithms.
Enhanced Credit Card Fraud Detection using Graph Neural Networks
Validates popular debit and credit cards numbers against regular expressions and Luhn algorithm. Also validates the CVC and the expiration date.
[TAAI'21] Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection
Credit Card Gen Luhn is a web application that generates valid credit card numbers using the Luhn algorithm. It's designed for educational and testing purposes, helping developers and QA teams simulate card inputs to validate forms, payment systems, or backend processing without using real data.
The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.
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