How to Build a Customizable Fraud Investigation Dashboard

Simple Summary

A customizable dashboard for fraud investigators that provides real-time monitoring of key metrics and data points, enabling efficient detection and analysis of fraudulent activities.

Product Requirements Document (PRD)

Goals:

1. Create a customizable dashboard for fraud investigators
2. Provide real-time monitoring of key fraud indicators
3. Enable efficient analysis and response to potential fraudulent activities
4. Ensure scalability and security of sensitive data

Target Audience:

• Fraud investigators in financial institutions, e-commerce, and other industries

Key Features:

1. Customizable widgets for different fraud metrics
2. Real-time data updates and visualizations
3. Alert system for anomalies and suspicious activities
4. User authentication and role-based access control
5. Data filtering and advanced search capabilities
6. Integration with external data sources and APIs
7. Reporting and export functionality

User Requirements:

1. Intuitive interface for easy customization
2. Fast loading and responsive design
3. Secure access to sensitive data
4. Ability to save and share dashboard configurations
5. Mobile-friendly for on-the-go access

User Flows

1. Dashboard Customization:

   • User logs in
   • Navigates to dashboard settings
   • Adds/removes widgets
   • Arranges widget layout
   • Saves custom configuration

2. Alert Investigation:

   • User receives alert notification
   • Clicks on alert to view details
   • Analyzes related data and visualizations
   • Takes action (e.g., flag for further investigation, dismiss)
   • Logs investigation notes

3. Report Generation:

   • User selects date range and metrics
   • Chooses report template
   • Generates report
   • Reviews and edits if necessary
   • Exports or shares report

Technical Specifications

• Frontend: React with Redux for state management
• Backend: Node.js with Express
• Database: PostgreSQL for structured data, MongoDB for unstructured data
• API: RESTful API with GraphQL for complex data queries
• Authentication: JWT (JSON Web Tokens) with OAuth 2.0
• Data Visualization: D3.js and Chart.js
• Real-time Updates: WebSockets
• Containerization: Docker
• CI/CD: Jenkins or GitLab CI
• Monitoring: ELK Stack (Elasticsearch, Logstash, Kibana)

API Endpoints

• POST /api/auth/login
• POST /api/auth/logout
• GET /api/dashboard/config
• POST /api/dashboard/config
• GET /api/metrics
• POST /api/alerts
• GET /api/reports
• POST /api/reports/generate
• GET /api/users
• PUT /api/users/:id

Database Schema

1. Users

   • id (PK)
   • username
   • email
   • password_hash
   • role
   • created_at
   • last_login

2. DashboardConfigs

   • id (PK)
   • user_id (FK)
   • config_json
   • created_at
   • updated_at

3. Metrics

   • id (PK)
   • name
   • description
   • data_source
   • update_frequency

4. Alerts

   • id (PK)
   • user_id (FK)
   • metric_id (FK)
   • threshold
   • condition
   • status

5. Reports

   • id (PK)
   • user_id (FK)
   • title
   • description
   • generated_at
   • file_path

File Structure

Implementation Plan

1. Project Setup (1-2 days)

   • Initialize React app and Node.js server
   • Set up version control and project structure
   • Configure development environment

2. Authentication and User Management (3-4 days)

   • Implement user registration and login
   • Set up JWT authentication
   • Create user roles and permissions

3. Dashboard Framework (5-7 days)

   • Develop basic dashboard layout
   • Create customizable widget system
   • Implement drag-and-drop functionality

4. Data Integration and Visualization (7-10 days)

   • Set up database connections
   • Implement data fetching and processing
   • Create visualizations for different metrics

5. Alert System (4-5 days)

   • Develop alert creation and management
   • Implement real-time notifications
   • Create alert investigation workflow

6. Reporting Module (4-5 days)

   • Design report templates
   • Implement report generation functionality
   • Add export and sharing features

7. Testing and Quality Assurance (5-7 days)

   • Write and run unit tests
   • Perform integration testing
   • Conduct user acceptance testing

8. Optimization and Security (3-4 days)

   • Optimize performance
   • Implement security best practices
   • Conduct security audit

9. Documentation and Deployment (2-3 days)

   • Write user and technical documentation
   • Prepare deployment scripts
   • Deploy to staging environment

10. Final Review and Launch (2-3 days)

    • Conduct final testing
    • Make necessary adjustments
    • Deploy to production

Total estimated time: 36-50 days

Deployment Strategy

1. Set up staging and production environments on a cloud provider (e.g., AWS, Google Cloud)
2. Use Docker containers for consistent deployment across environments
3. Implement CI/CD pipeline using Jenkins or GitLab CI
4. Use Kubernetes for container orchestration and scaling
5. Set up automated database backups and replication
6. Implement monitoring and logging with ELK Stack
7. Use CDN for static asset delivery
8. Implement SSL/TLS encryption for all communications
9. Set up auto-scaling rules based on traffic patterns
10. Conduct regular security audits and penetration testing

Design Rationale

The design decisions for this fraud investigation dashboard prioritize flexibility, performance, and security. React was chosen for the frontend due to its component-based architecture, which aligns well with the customizable widget system. Node.js on the backend provides a JavaScript-based full-stack solution, enabling efficient development.

The combination of PostgreSQL and MongoDB allows for handling both structured and unstructured data, which is crucial for fraud investigation where data formats can vary. Real-time updates using WebSockets ensure investigators always have the latest information.

The modular file structure and use of containers facilitate easier maintenance and scalability. The implementation plan focuses on iterative development, allowing for early testing and feedback. The deployment strategy emphasizes security and high availability, which are critical for a system handling sensitive financial data.