How to Build a Dynamic Credit Analyst Dashboard

Simple Summary

A customizable dashboard for credit analysts that streamlines data visualization and decision-making processes, enhancing efficiency and accuracy in credit risk assessment.

Product Requirements Document (PRD)

Goals:

• Develop a user-friendly, customizable dashboard for credit analysts
• Provide real-time data visualization of key credit metrics
• Enable efficient risk assessment and decision-making
• Ensure data security and compliance with financial regulations

Target Audience:

• Credit analysts in banks, financial institutions, and lending companies
• Risk management teams
• Financial decision-makers

Key Features:

1. Customizable widgets for different credit metrics
2. Real-time data integration from multiple sources
3. Interactive charts and graphs for trend analysis
4. Risk scoring and alert system
5. Collaborative tools for team decision-making
6. Secure user authentication and role-based access control
7. Exportable reports and data

User Requirements:

• Intuitive interface for easy customization
• Fast loading and responsive design
• Mobile compatibility for on-the-go access
• Data accuracy and reliability
• Compliance with data protection regulations

User Flows

1. Dashboard Customization:

   • User logs in
   • Selects "Customize Dashboard" option
   • Chooses widgets from available options
   • Arranges widgets on the dashboard
   • Saves layout

2. Risk Assessment:

   • User views key risk indicators on dashboard
   • Clicks on a specific metric for detailed view
   • Analyzes historical trends
   • Sets up alerts for specific thresholds
   • Generates risk report

3. Collaborative Decision-Making:

   • User identifies a high-risk case
   • Initiates a team review process
   • Shares dashboard view with team members
   • Conducts virtual meeting using integrated tools
   • Records decision and justification

Technical Specifications

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

API Endpoints

• /api/auth/login
• /api/auth/logout
• /api/dashboard/widgets
• /api/metrics/credit-score
• /api/metrics/default-risk
• /api/metrics/liquidity-ratio
• /api/alerts
• /api/reports/generate
• /api/collaboration/share
• /api/user/preferences

Database Schema

Users Table:

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

Widgets Table:

• id (PK)
• name
• type
• config_json

UserDashboards Table:

• id (PK)
• user_id (FK to Users)
• layout_json

CreditMetrics Table:

• id (PK)
• metric_name
• value
• timestamp
• source

Alerts Table:

• id (PK)
• user_id (FK to Users)
• metric_id (FK to CreditMetrics)
• threshold
• condition

File Structure

Implementation Plan

1. Project Setup (1 week)

   • Initialize React project
   • Set up Node.js backend
   • Configure databases
   • Implement basic authentication

2. Core Dashboard Development (3 weeks)

   • Create basic dashboard layout
   • Implement widget system
   • Develop data fetching and state management

3. Data Visualization (2 weeks)

   • Integrate D3.js and Chart.js
   • Create various chart types for credit metrics
   • Implement real-time data updates

4. Risk Assessment Features (2 weeks)

   • Develop risk scoring algorithms
   • Create alert system
   • Implement threshold setting functionality

5. Collaboration Tools (1 week)

   • Develop dashboard sharing feature
   • Implement commenting system
   • Create decision logging functionality

6. Reporting and Exports (1 week)

   • Develop report generation feature
   • Implement data export functionality

7. Testing and QA (2 weeks)

   • Conduct unit and integration testing
   • Perform user acceptance testing
   • Bug fixing and performance optimization

8. Deployment and Documentation (1 week)

   • Prepare deployment scripts
   • Set up CI/CD pipeline
   • Write user and technical documentation

Deployment Strategy

1. Set up staging and production environments on chosen cloud platform
2. Configure Docker containers for consistent deployments
3. Implement CI/CD pipeline for automated testing and deployment
4. Use blue-green deployment strategy to minimize downtime
5. Set up database replication and backups
6. Implement logging and monitoring solutions
7. Conduct security audits and penetration testing
8. Establish a rollback strategy for critical issues
9. Plan for scalability with load balancers and auto-scaling groups

Design Rationale

The design focuses on flexibility and efficiency for credit analysts. React was chosen for its component-based architecture, allowing for easy customization of dashboard widgets. Node.js provides a fast, scalable backend. PostgreSQL offers robust support for financial data, while MongoDB allows for flexible storage of unstructured data.

The use of D3.js and Chart.js provides powerful, interactive data visualizations crucial for credit analysis. WebSockets enable real-time updates, ensuring analysts always have the latest information. The modular file structure and use of modern JavaScript practices (hooks, Redux) promote maintainability and scalability.

Security is prioritized through JWT authentication and role-based access control, crucial for handling sensitive financial data. The deployment strategy emphasizes reliability and quick recovery, essential for a tool used in critical financial decision-making.