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Loan-Default-Risk-Control

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Introduction

"Loan-Default-Risk-Control" is a sophisticated Streamlit web application focused on loan default risk management. This app features an advanced logistic regression scorecard model for the automated evaluation of loan default risks, enabling efficient, data-driven decisions in financial risk assessment.

Key Features

  • Automated Risk Score Assessment: Utilizes a logistic regression scorecard for quick and accurate risk evaluations.
  • Threshold-Based Automated Decision Making: Approves or rejects loan applications based on set risk score thresholds, optimizing decision-making speed and accuracy.
  • Manual Review Indication: Flags applications that need further human evaluation, ensuring comprehensive risk assessment.
  • Interactive and User-Friendly Interface: Inputs are easily managed via a sidebar, allowing for streamlined data entry and interaction.
  • Real-Time Processing and Analysis: Capable of processing input data in real-time for immediate risk scoring and decision-making.

How to Use

  1. Access the App: Go to Loan-Default-Risk-Control on the Streamlit Community Cloud.
  2. Input Data via Sidebar: Use the sidebar to input loan-related information such as loan amount, term, grade, annual income, and other financial details.
  3. Submit for Risk Assessment: After data input, the app processes the information using the scorecard model and computes a risk score.
  4. Automated Loan Decision:
    • Applications exceeding the risk score threshold are automatically approved.
    • Applications below the threshold are automatically rejected.
    • Applications close to the threshold are flagged for manual review.
  5. View Assessment Summaries: Navigate to a separate page within the app to view all assessment summaries and comprehensive results.

Technical Details

  • Built With: Developed using Python, featuring libraries like Pandas, NumPy, scikit-learn for data handling, machine learning operations, and Plotly for interactive visualizations.
  • Scorecard Model: Integrates logistic regression for calculating risk scores, which are essential for automated decision-making.
  • Dynamic Data Processing: Implements efficient feature engineering and real-time data processing for immediate risk analysis.

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