Compliance Risk Radar

Hi, I'm Charlotte Evason

Welcome to the documentation for Compliance Risk Radar — a comprehensive fraud detection dashboard I built for visualizing risk scores, identifying anomalies, and monitoring high-risk users across global markets.

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Project Overview
A sophisticated fraud detection and compliance monitoring system

Compliance Risk Radar is an interactive dashboard designed to help compliance teams identify and monitor potential fraud risks across user bases. The system processes user data to generate risk scores, detect anomalies, and provide geographic insights into suspicious activities.

Key Capabilities

  • • Real-time risk score calculation
  • • Anomaly detection and flagging
  • • Geographic risk visualization
  • • Interactive filtering and analysis

Technologies Used

  • • Python & Pandas for data processing
  • • Streamlit for dashboard interface
  • • Plotly for interactive visualizations
  • • Machine learning for risk scoring

Dashboard Features

Risk Score Analytics
Comprehensive risk assessment and scoring system

Advanced algorithms analyze user behavior patterns, transaction history, and account characteristics to generate accurate risk scores.

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Anomaly Detection
Intelligent identification of suspicious patterns

Machine learning models detect unusual user behaviors and flag potential fraud cases for manual review.

View Filtering Options
Geographic Insights
Global risk visualization and mapping

Interactive heatmaps show risk distribution across countries and regions, helping identify geographic fraud patterns.

Explore Heatmaps
User Monitoring
Comprehensive user profile analysis

Track KYC status, account age, transaction patterns, and other key indicators for each user in the system.

View Data Schema
Quick Start Guide
Get started with the Compliance Risk Radar dashboard
1

Launch Dashboard

Click the "Launch Dashboard" button to open the interactive Streamlit application.

2

Explore Risk Scores

Review the risk score distribution and identify high-risk users requiring attention.

3

Apply Filters

Use the filtering options to focus on specific countries, risk thresholds, or anomaly flags.

Technical Highlights
Key technical achievements and implementation details

Data Processing

  • • Processed and cleaned large-scale user datasets
  • • Implemented feature engineering for risk scoring
  • • Built automated data validation pipelines
  • • Optimized performance for real-time analysis

Visualization & UX

  • • Created interactive geographic heatmaps
  • • Designed intuitive filtering interfaces
  • • Built responsive dashboard layouts
  • • Implemented real-time data updates