FRONTEND DEMO - This is a static demonstration. For full functionality, deploy the backend server.

FRAUD EVASION ASSESSOR

Detection Resilience Testing - Educational Demo

About This Demo

This is a client-side demonstration that simulates fraud detection scoring. All logic runs in your browser. For production use, deploy the full backend.

Purpose: Educational tool for understanding fraud detection concepts.

1. Target Assessment

The payment gateway or checkout page being assessed.

2. Connection & Geolocation

Distance between billing address and transaction IP.
Distance from last known transaction location.

3. Card Configuration

E.g., $500 / $50 median = 10.0

Assessment Result

0% Detection Resilience Score
READY TO TEST

Vulnerability Report

  • Configure parameters and run the test to generate a report.

Quick Test Presets

Documentation & Methodology

How Fraud Detection Works

Modern fraud detection systems use Machine Learning (ML) models to analyze thousands of data points in real-time, looking for anomalies that deviate from normal behavior.

This tool simulates a Random Forest Classifier trained on credit card transactions to evaluate fraud detection resilience.

Key Risk Factors

  • Geolocation Anomalies: High distances between billing address and transaction IP.
  • Velocity Checks: "Impossible travel" between transaction locations.
  • Spending Patterns: Transactions above normal spending thresholds.
  • Authentication Strength: Chip & PIN provides strongest verification.

Understanding the Score

Detection Resilience Score = 1.0 - Fraud Probability

  • High (>90%): Highly likely to bypass controls.
  • Medium (50-90%): May be flagged for review.
  • Low (<50%): Exhibits multiple risk factors.