Bank card fraud detection using machine learning. Web application using Streamlit framework

M5Stack Cardputer interface for the FraudTagger API

Ethereum fraud transaction detection using machine learning

To identify online payment fraud with machine learning, we need to train a machine learning model for classifying fraudulent and non-fraudulent payments. For this, we need a dataset containing information about online payment fraud, so that we can understand what type of transactions lead to fraud.

Fraud Detection for e-commerce and Bank Transactions

🛡️ 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! 🔐💯

This project demonstrates the use of a Self-Organizing Map (SOM) for fraud detection in a dataset. The dataset contains transaction records, and the goal is to identify potential fraudulent transactions using unsupervised learning techniques.

End-to-end MLOps: SageMaker training, Model Registry, CI/CD, Guardrails, API Gateway.

ML-based system to detect fraudulent credit card transactions with cost-benefit analysis.

Machine learning projects focused on fraud detection.

ENSAE-ENSAI Formation Continue (Cepe)/OpenClassrooms Data Analyst 2022-2023 - Projet 10

Python note book for fraud detection and risk assessment in finance section

This is a fraud detection algorithm that is designed to detect fraudulent activity in online in-game markets using a combination of unsupervised learning and a rule based approach.

An XGBoost-based fraud detection modelto identify money laundering in mobile transactions using PaySim synthetic dataset.

Advanced Fraud Detection for E-Commerce and Banking This project focuses on detecting fraudulent transactions in e-commerce and banking using machine learning. It includes data preprocessing, feature engineering, model training, and deployment with Flask and Docker. Geolocation analysis and transaction pattern recognition enhance fraud detection.

Built a Fraud Detection System using ANN to identify fraudulent transactions with high accuracy. Optimized feature engineering and model performance, ensuring robust anomaly detection. Documented the process in Jupyter Notebooks for transparency and reproducibility.

Credit Score Classification: This repository features a machine learning project aimed at predicting credit scores based on financial data. Using advanced models like Random Forest and Gradient Boosting.

Advanced anomaly detection system using graph neural networks and time series analysis to identify fraudulent transactions, money laundering patterns, and market manipulation in real-time financial data streams.

A credit card fraud detection system using Machine learning (Early project-2022)

Graph neural network system that detects money laundering, fraud patterns, and security threats in blockchain transactions and smart contracts.