This is an Online Transaction Fraud Detection System (FDS) to detect payment frauds. Made using Django.

This repository outlines various solutions using AWS Cloud's AIML services to detect fraud faster.

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.

AI-powered behavioural fraud detection system for UPI transactions using FastAPI and Streamlit.

Real-time AML transaction scoring pipeline on AWS: Lambda, S3, DynamoDB, SNS, IAM Roles and Terraform (IaC)

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

Fraud investigation tutorial across 9 phases — same 6 cases, progressively adding LangGraph, tools, HITL, multi-agent coordination, and LangSmith observability

End-to-end fraud detection — JAX neural nets from scratch, multi-objective Optuna (Pareto recall vs precision), custom Precision@K/Recall@K/Lift@K metrics, SHAP explainability, and GitHub Actions CI/CD. No ML framework shortcuts.

data-engineering aws pyspark fraud-detection data-pipeline etl-pipeline aws-glue data-lake medallion-architecture banking-analytics

An open-source project for ROSP Lab based on Fraud Detection using ML

Real-time transaction risk monitoring system with rule-based fraud detection, REST API, and a React dashboard. Built with Node.js, Express, and SQLite.

AI Deepfake and Fraud Detection

Full-stack Flask insurance workflow system with policy management, QR verification, Twilio SMS alerts, NLP sentiment analysis, and Decision Tree-based fraud detection (77% accuracy).

Detect fraudulent transactions and accounts in a vast dataset

A ML-based web app that detects fraudulent e-commerce transactions in real time. Features SMOTE for class balancing, Logistic Regression & Random Forest models, and an interactive form UI for live predictions. Built with Python, scikit-learn, and Flask.