fraud

Browser fingerprinting library with the highest accuracy and stability.

💰 Billing & Payments Knowledge for Cloud Platforms

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

Modern Web Firewall: stop account takeovers, weak passwords, cloud IPs, DoS attacks, disposable emails

Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!

"Very simple but works well" Computer Vision based ID verification solution provided by LibraX.

DNS Block Lists

PresentID Face Liveness Detection API

Decentralized Social Network Money Frauds/Scams including BitClout, Twetch, Steemit, PeakD

Case Studies on Forensic Accounting using Data Analysis

🚨 Hoaxwagen detects Volkswagen in dependencies to prevent tests fraud

Classification of fraudulent credit card transactions.

Music and text generation with Transformer-XL.

Protect online forms from spam by making them perform Proof of Work (PoW).

The security filter for AdGuard Home🦟

Ten Simples Rules for Scientific Fraud and Misconduct

MyAdmin MaxMind Fraud Plugin

Code example used in the tutorial video for preventing multiple signups with FingerprintJS

An online directory to list spammers, frauds or scammers. Search spammers by keywords. Report spammers in spamsort and share them to the world!

MyAdmin FraudRecord Fraud Plugin

using HMM to detect credit card fraudulent transaction

Implementation of an intelligence system to detect the fraud cases on the basis of classification.

Fraud Detection in Transactions analysis using Deep Learning and exploiting Deep Transfer Learning technique for it.

A fraud analysis tool used to check for invalid and disposable email domains.

This repository contains the overview of all the approaches that we are applying in order to detect Fraud

Classifying Credit Card transactions as fraudulent or genuine using Classification techniques.

Fraud & data analysis on E-commerce transactions data set

Detecting fraud in real-time using machine learning and data analysis. Web app for ease of use.

An analysis of the released data on FinCrime Files transactions as depicted on SARs.

Card-Not-Present Fraud Project: in an imbalanced dataset containing 200,000 financial transactions, I was asked with identifying 400 rows of fraud. This Random Forest model successfully caught about 75% true positives, with only 0.00025% false positives.