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

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

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.

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

AI-powered deepfake detection system using Deep Learning and Computer Vision to identify manipulated facial images and videos with high accuracy.

AI Deepfake and Fraud Detection

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.

Generative AI-powered financial fraud detection system built with Google AI Studio and Gemini API.

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

A high-speed candidate ranking platform and recruiter workspace that solves resume fraud and inaccurate skill matching through automated timeline verification and a semantic knowledge graph.

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

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).

We address the 'Resilience Gap' in modern autonomous systems. As enterprises transition from AI tools to Agentic AI, the risk of operational drift and systemic capture increases exponentially. Our mission is to provide the Cohesion Layer necessary for secure, sovereign execution.

Credit Card Fraud Detection using SMOTE, XGBoost, and LightGBM.

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

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.