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FIGMENT - AI-Powered UPI Fraud Detection System

Problem Statement

UPI (Unified Payments Interface) transactions have become increasingly popular in India, but this growth has also led to a rise in fraudulent activities. Users often fall victim to scams during transactions, resulting in financial losses. The challenge is to detect and prevent these fraudulent transactions in real-time before they are completed.

Current solutions lack:

  • Real-time fraud detection capabilities
  • Multi-factor analysis of transaction patterns
  • User behavior monitoring
  • Immediate intervention mechanisms

Our Solution

FIGMENT is a multi-agent AI system designed to detect potential scams during UPI transactions in real time. Our approach combines multiple specialized AI agents that analyze different aspects of a transaction:

  1. Pattern Agent: Analyzes transaction messages using Machine Learning (TF-IDF + Logistic Regression)
  2. Network Agent: Checks receiver UPI against a crowd-reported scam database in MongoDB
  3. Behavior Agent: Detects anomalies in user behavior using Isolation Forest algorithms
  4. Biometric Agent: Monitors typing patterns (speed, corrections) for signs of coercion

The system aggregates results from all agents into an overall risk score. For high-risk transactions (>40% risk), the system requires PIN confirmation before proceeding.

Additionally, Gemini AI generates human-readable explanations for why a transaction was flagged, helping users understand the risks.

Implementation Approaches

We have developed FIGMENT using two different approaches:

1. Web Application

A full-featured web application built with React and Flask that provides a comprehensive dashboard for monitoring transactions and viewing fraud analysis.

Technology Stack:

  • Frontend: React, TailwindCSS, Framer Motion, shadcn/ui
  • Backend: Flask, MongoDB, scikit-learn
  • AI Integration: Google Generative AI (Gemini)

2. Mobile Application

A mobile-first implementation designed for on-the-go transaction monitoring and immediate fraud alerts.

Getting Started

For detailed instructions on setting up and running each version, please refer to the respective README files:

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