fraud-detection-using-machine-learning

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

M5Stack Cardputer interface for the FraudTagger API

Fraud Detection for e-commerce and Bank Transactions

A data science project focused on identifying fraudulent transactions in highly imbalanced datasets using Python and Scikit-Learn.

Advanced Credit Card Fraud Detection System using XGBoost, SMOTE, and SHAP — built with Streamlit for real-time prediction, batch analysis, and explainable AI visualizations.

Ethereum fraud transaction detection using machine learning

Real-time UPI fraud detection system (0.8953 ROC-AUC) with <500ms FastAPI scoring, 480+ temporal features, and budget-aware alerts under fintech constraints

A machine learning-based fraud detection system that analyzes transaction patterns to identify potentially fraudulent activities. Features a Streamlit web interface for real-time predictions. Note: Model is currently in development with ongoing improvements planned.

An integrated web app merging a learning management system and online examination platform, enhanced with AI-enabled proctoring for fraud detection during exams.

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.

roduction-oriented fraud detection system with leakage-free stateful behavioral features, XGBoost inference, model lifecycle management, real-time decisioning, and full-stack observability.

🛡️ 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! 🔐💯

A full-stack phishing and fraud risk analysis system with FastAPI endpoints for scanning URLs, emails, social text, QR codes, bulk URLs, and transactions. It returns explainable outputs including risk score, label, indicators, and educational guidance. The scoring engine combines heuristic indicators with model probabilities.

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.

An end-to-end MLOps project for credit card fraud detection. Features a cost-sensitive hybrid ensemble model, real-time monitoring dashboard with Streamlit, automated PDF fraud reporting, SHAP/LIME explainability, and proactive data drift detection.

Modular fintech intelligence system with authentication, ML-based risk detection, anomaly analysis, live NSE/BSE tracking, and stock price prediction.

Hybrid deep learning fraud detection system using CNN, BiLSTM, and Attention mechanisms for UPI transaction classification.

An interactive machine learning web application built with Streamlit to predict and detect fraudulent financial transactions based on transfer amounts, transaction types, and account balances.

A full-stack Banking Management System built with Flask and MySQL, integrated with Machine Learning for real-time transaction fraud detection, risk scoring, complete CRUD management (Customers, Accounts, Loans, Cards, Employees), interactive analytics, and automated PDF/Excel reports.

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