Credit Risk Dashboard with Real-Time Data Analysis

Developed a full-stack credit risk analysis platform for financial institutions to assess borrower creditworthiness. The application provides real-time analytics on borrower profiles, predicts default risks using machine learning models, and features an interactive dashboard for visualizing credit risk trends and running simulations.

Credit Risk Dashboard with Real-Time Data Analysis

Project Overview: Developed a full-stack credit risk analysis platform for financial institutions to assess borrower creditworthiness. The application provides real-time analytics on borrower profiles, predicts default risks using machine learning models, and features an interactive dashboard for visualizing credit risk trends and running simulations.

Key Features:

  • Real-Time Risk Assessment: Processes large volumes of borrower data to assess credit risk in real-time.
  • Interactive Dashboard: Allows users to visualize borrower risk scores and track credit trends over time.
  • "What-If" Simulations: Enables users to simulate economic scenarios and observe their impact on borrower risk.

Technologies Used:

  • Backend: Java with Spring Boot for API development and data processing.
  • Frontend: React for building a responsive and interactive user interface.
  • Machine Learning: TensorFlow/Deeplearning4J for developing credit risk prediction models.
  • Database: MongoDB for storing borrower profiles and risk scores.
  • Cloud: Hosted on AWS/Azure for scalable machine learning and backend services.
  • CI/CD: Jenkins/Azure Pipelines for automating testing and deployment.

This project demonstrates my ability to build secure, scalable applications that utilize cloud technologies, machine learning, and full-stack development, aligning with the high standards of the financial services industry.