Credit Risk Workbench

Overview

Credit Risk Workbench is a desktop-based analytical application designed to support the end-to-end development of traditional credit risk scorecards. It provides a structured, transparent, and practitioner-focused workflow for building, analyzing, and exporting credit risk models commonly used in banking and financial institutions.

The application emphasizes interpretability, statistical rigor, and regulatory transparency, aligning with industry-standard scorecard development methodologies rather than black-box modeling approaches.

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Application Purpose

The primary objective of Credit Risk Workbench is to provide credit risk practitioners with a controlled and explainable environment for:

• Developing scorecards using established statistical techniques
• Inspecting and refining binning logic
• Evaluating variable-level and model-level performance
• Exporting modeling artifacts for validation, governance, and system integration

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Core Features

1. Data Ingestion

• Load separate training and validation datasets
• Automatic validation of dataset structure and consistency
• Enforces binary target definition:
  • 0 → Non-target
  • 1 → Target
• Displays dataset dimensions and class distribution

Screenshot — Data Ingestion Screen

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2. Variable Selection

• Interactive variable selection interface
• Search, filter, and bulk selection capabilities
• Clear distinction between target variable and predictors
• Ensures consistency across training and validation datasets

Screenshot — Variable Selection Screen

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3. Supervised Binning (WoE / IV)

• Supervised Weight of Evidence (WoE) and Information Value (IV) based binning
• Enforces monotonicity, consistent with scorecard best practices
• Supports both:
  • Numerical variables
  • Categorical variables
• Automated binning with practitioner oversight

Screenshot — Automatic Binning Screen

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4. Manual Bin Adjustment

• Ability to manually split and merge bins
• Supports expert-driven refinement of automated binning
• Immediate recalculation of bin-level statistics
• Persistent binning state to avoid unnecessary recomputation

Screenshot — Manual Binning Screen

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5. Statistical Diagnostics

For each binned variable, the application computes and presents:

• Weight of Evidence (WoE)
• Information Value (IV)
• Area Under the Curve (AUC)
• Gini coefficient
• Target and non-target distributions per bin

These diagnostics support:

• Variable selection decisions
• Model explainability
• Validation and governance requirements

Screenshot — Variable Statistics Screen

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6. Scorecard Development

The Scorecard Development module is organized into three dedicated tabs, guiding the user through model setup, scorecard results, and model diagnostics.

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6.1 Model Setup

• User-driven selection of variables to be included in the final scorecard model from the set of successfully binned predictors
• Explicit control over scorecard scaling parameters, including:
  • Base Score
  • Points to Double the Odds (PDO)
  • Base Odds
• Construction of a traditional logistic regression–based credit scorecard using Weight of Evidence (WoE)–transformed variables
• Clear and auditable transformation pipeline:
  • Bins → Weight of Evidence (WoE)
  • WoE → Logistic regression coefficients
  • Coefficients → Scorecard points

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6.2 Scorecard Results

This tab presents model performance and scorecard outputs for both training and validation datasets, including:

Model Performance Metrics

• Observation count for training and validation samples
• Kolmogorov–Smirnov (KS) statistic
• KS score
• Area Under the ROC Curve (AUC)
• Gini coefficient

Scorecard Output

• Detailed scorecard table including:
  • Variable bin definitions
  • Weight of Evidence values
  • Variable Coefficient
  • Point allocation per bin
  • Bin Counts and Target rate

Export Capabilities

• Export of the complete scorecard summary as an Excel file
• Export of the trained model as:
  • PMML for system integration
  • JSON containing detailed scorecard binning rules for transparency and reproducibility

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6.3 Model Diagnostics

This tab provides statistical and visual diagnostics to support model validation and governance.

Statistical Diagnostics

• Coefficient significance analysis, including p-values for model coefficients
• Correlation matrix of model coefficients to assess multicollinearity

Diagnostic Plots

• AUC Curve
• KS Chart (validation dataset)
• Calibration chart to assess odds-to-score alignment
• Score distribution plots to evaluate separation and stability

These diagnostics enable practitioners to evaluate model robustness, stability, and interpretability prior to deployment.

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Application Architecture

• Frontend: PyQt6 desktop GUI
• Design Pattern: Controller-based modular architecture
• Backend: Python-based statistical computation
• Packaging: PyInstaller (Windows executable); Working on Linux and macOS support for future releases

The architecture prioritizes clarity, separation of concerns, and traceability, reflecting real-world credit model development workflows.

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Running the Application

Option 1: Run from Source (Developers)

python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
python app.py

Option 2: Run Packaged Executable (Windows)

CreditRiskWorkbench.exe

Technology Stack

• Python 3.x
• PyQt6
• pandas
• numpy
• scikit-learn
• matplotlib and seaborn
• PyInstaller

Intended Use & Scope

This application is designed for:

• Retail credit risk modeling
• Scorecard development and validation workflows
• Educational and research purposes
• Environments requiring model interpretability, auditability, and regulatory transparency

The Credit Risk Workbench focuses on traditional, interpretable scorecard modeling techniques and does not aim to replace enterprise-wide model governance or production scoring platforms.

References Texts

• Siddiqi, N. (2012). Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards. Wiley & SAS Business Series.