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

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

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

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

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

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.

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.

🛡️ 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.

Binary classifier with Venn-ABERS calibration, temporal validation, hyper-parameter optimisation, and fraud detection out of the box

A machine learning pipeline that detects fraudulent transactions using a Random Forest Classifier on synthetic data.

ML project to detect fraudulent job postings using NLP & Scikit-learn

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

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

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