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.

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.

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

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

🛡️ 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! 🔐💯

Production-oriented fraud detection system using sequence modeling (CRNN + Attention) with real-time inference capabilities.

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.

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

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.

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

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

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 MLOps: SageMaker training, Model Registry, CI/CD, Guardrails, API Gateway.