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FairScore

AI-Powered Credit Scoring System with Bloomberg Terminal-Style TUI

FairScore is a fair, explainable, and inclusive credit scoring system that uses behavioral financial data instead of traditional credit bureau metrics. It features a Terminal User Interface (TUI) inspired by Bloomberg Terminal aesthetics.

Features

  • 4 Specialized ML Models: Income Stability (LightGBM), Expense Discipline (Random Forest), Liquidity Buffer (Logistic Regression), Risk Detection (Isolation Forest)
  • FAIR-SCORE Formula: Weighted ensemble producing 300-900 score range
  • Explainability: SHAP-based explanations with waterfall visualizations
  • Dual Input Modes: PDF parsing (Gemini AI) or manual data entry
  • Bloomberg Terminal UI: Phosphor green on matte black aesthetic

Installation

# Clone or download, then:
pip install -e .

Quick Start

# Launch TUI
fairscore

# Train models (generates synthetic data first)
python -m fairscore.models.trainer

# Generate synthetic data only
python -c "from fairscore.data import DataGenerator; DataGenerator().generate_all()"

Keyboard Shortcuts

Key Action
F1 Help screen
F5 Refresh display
Ctrl+O Upload PDF
Ctrl+M Manual entry
Tab Navigate fields
Q Quit

Score Categories

Range Category Description
750-900 EXCELLENT Best creditworthiness
650-749 GOOD Above average
500-649 FAIR Average
300-499 POOR Below average

Project Structure

fairscore/
  __init__.py       # Package initialization
  app.py            # Main TUI application
  cli.py            # CLI entry points
  config.py         # Configuration management
  theme.py          # Bloomberg Terminal theme
  data/
    generator.py    # Synthetic data generation
    features.py     # Feature engineering
  models/
    income_stability.py    # LightGBM model
    expense_discipline.py  # Random Forest model
    liquidity_buffer.py    # Logistic Regression model
    risk_anomaly.py        # Isolation Forest model
    ensemble.py            # FAIR-SCORE computation
    trainer.py             # Training pipeline
  explainability/
    shap_explainer.py      # SHAP explanations
  parser/
    pdf_parser.py          # Gemini AI PDF parsing
  ui/
    dashboard.py           # Main dashboard
    model_matrix.py        # 4-panel model display
    score_gauge.py         # ASCII score gauge
    input_mode.py          # Input selection/forms

Configuration

Set environment variable for PDF parsing:

set GOOGLE_API_KEY=your_gemini_api_key

License

MIT License

Metadata

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