Open-source, real-time Anti-Money Laundering (AML) transaction monitoring.

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

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

This project focuses on detecting fraudulent credit card transactions using machine learning techniques. The goal is to predict whether a given transaction is legitimate or fraudulent based on various features of the transaction.

🛡️ 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.

A machine learning project for detecting fraudulent transactions using Random Forest and XGBoost models, with data preprocessing and model evaluation.

Detecting fraudulent credit card transactions using machine learning techniques, with a focus on handling imbalanced datasets.

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.

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.

A machine learning-based fraud detection system that preprocesses data, manages outliers, handles missing values, and mitigates multi-collinearity. It utilizes predictive modeling techniques (Logistic Regression, Random Forest, Gradient Boosting) and evaluates performance using precision, recall, F1-score, and ROC-AUC.

Fraud detection in Insurance Companies

Machine learning-based fraud detection system capable of identifying and preventing fraudulent transactions in real-time for Finex, a financial service provider based in Florida.

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