Marble - the real time decision engine for fraud and AML
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Updated
May 27, 2024 - HCL
Marble - the real time decision engine for fraud and AML
MISP (core software) - Open Source Threat Intelligence and Sharing Platform
A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
🎁 Blocks browser-based crypto mining, cryptojacking, banking and crypto malware and phishing websites, apps and hackers command-and-control (C2) servers.
Data science projects at Aboitiz
Distributed Networks Institute
An iOS application that showcases the capabilities of Fingerprint Identification SDK.
Browser fingerprinting library. Accuracy of this version is 40-60%, accuracy of the commercial Fingerprint Identification is 99.5%. V4 of this library is BSL licensed.
A Flutter plugin for the native FingerprintJS Pro libraries
StalkPhish-OSS - The Phishing kits stalker, harvesting phishing kits for investigations.
This project aims to develop a machine learning model for proactive fraud detection in financial transactions.
A machine learning project for detecting fraudulent transactions in fintech banking systems. Includes data preprocessing, feature engineering, and model evaluation.
Sift (fraud detection) integration with Magento 2
🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯
Fingerprint Pro Plugin for Vue
This system utilizes Optical Character Recognition (OCR) extracts text, while computer vision techniques map document layout. Then, SIFT (Scale-Invariant Feature Transform) cleverly matches documents to pre-defined templates, even with variations. This intelligent matching helps identify potential fraud for further investigation.
Protect your SIP Servers from bad actors at https://sentrypeer.org
Official React Native client for Fingerprint PRO. 100% accurate device identification for fraud detection.
En este proyecto se desarrolló un modelo de Machine Learning para la Detección de Transacciones Fraudulentas, se trabajó desde la extracción, procesamiento y exploración de los datos
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