Scanner, signatures and the largest collection of Magento malware

Open-source ad fraud detection for small businesses using machine learning. Detect click fraud and bot traffic from Google Ads, Facebook Ads, and other platforms. Completely free.

This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.

MER is a software that identifies and highlights manipulative communication in text from human conversations and AI-generated responses. MER benchmarks language models for manipulative expressions, fostering development of transparency and safety in AI. It also supports manipulation victims by detecting manipulative patterns in human communication.

🔍 | 📈 | Life / Health Insurance Fraud Detection | 📋 | (Codeshahstra Round 1 Hackathon)

This solution performs Anomaly Detection with Statistical Modeling on Spark. The detection is based on Z-Score calculated on cpu usage data collected from servers.

DAFU is a comprehensive fraud detection and e-commerce analytics platform designed for enterprise deployment. Currently in active development, it provides advanced machine learning-based fraud detection capabilities with a focus on anomaly detection and sequence analysis. C: dafu@masterfabric.co

🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.

Report on the performance of different machine learning algorithms in identifying persons of interest in the Enron Fraud Case

A machine learning-based web application to detect financial fraud in real time. Users can input transaction details and get instant fraud predictions.

Fraud Detection Research - Data Science Capstone Project at Penn State University, University Park Campus

This repository contains the code components of work carried out for analyzing the Medical Provider Fraud Detection dataset with the intent to find most important features to crack down the potentially fraud providers.

This GitHub repository provides a comprehensive set of tools and algorithms for detecting fraud anomalies in various data sources. Fraudulent activities can have severe consequences, impacting businesses and individuals alike. With this repository, we aim to empower researchers with effective techniques to identify and prevent fraudulent behavior.

FraudDetect-AI-with flask app - WebApp with AI models

A sample website integrating the ComplyCube SDK.

Detecting fraud on online customer transactions

This project uses machine learning models like Logistic Regression, Random Forest, and XGBoost to detect fraudulent credit card transactions. It handles class imbalance using SMOTE and visualizes key fraud patterns through an interactive Power BI dashboard.

Ambriel Anti Fraud & Aml Complience Documentation

Using R Language to predict whether a user will download an app after clicking a mobile app advertisement. Click on the link below to see more details!

The goal of the competition was to predict fraudulent transactions on a dataset with about 40 million instances, with some characteristics similar to the datasets processed by Feedzai.