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

M5Stack Cardputer interface for the FraudTagger API

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

Fraud Detection for e-commerce and Bank Transactions

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.

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.

🛡️ 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! 🔐💯

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

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.

Fraud Detection REST API project built with FastAPI and LightGBM Binary Classifier.

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.

Mobile money fraud detection — EDA and baseline ML models for Ghana

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.

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

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.

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

End-to-end fraud detection system using machine learning with a Streamlit-based prediction interface