The Money Mule Detection System is an AI-driven intelligence platform designed to identify individuals and accounts being exploited as intermediaries in illegal financial activities. It continuously analyzes transactional behavior, account networks, device fingerprints, geographic patterns, and relationship graphs to detect anomalies that indicate mule activity, such as sudden spikes in inbound transfers, rapid pass-through of funds, links to known fraud clusters, or coordinated movement across multiple accounts. By combining machine-learning models with agentic reasoning, the system not only flags high-risk accounts but also explains the underlying patterns, enabling financial institutions and security agencies to intervene early, disrupt criminal networks, and safeguard the integrity of the national financial ecosystem.

This is a full-stack web application built with React.js on the frontend and Django REST Framework on the backend. It enables pet insurance service providers to run their operations end-to-end, including policy management, claims processing, customer onboarding, and administrative workflows. Depending on the client’s needs, I can deploy the complete system or deliver and integrate only the backend or frontend components.

In this project I developed the website for the coding school, the teacher dashboard, student portal and the admin dashboard using ReactJs for frontend, supabase for data management and Django Rest Framework for backend.

REMS is a web-based property management platform which allows landlords and real-estate agents to onboard themselves and manage their properties. The platform is built using Python and Django as a full-stack application.