Credit Risk Modeling: End-to-End Quantitative Framework

A production-grade credit risk modeling pipeline implementing Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), credit scorecard development, macroeconomic stress testing, and Basel III regulatory capital calculations.

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Project Overview

This project builds a complete credit risk analytics framework from the ground up:

Component	Description
Data Generation	Synthetic portfolio of 50,000 consumer loans with realistic correlations
EDA	6+ diagnostic visualizations with statistical summaries
Feature Engineering	35+ features including ratios, interactions, and log transforms
PD Models	Logistic Regression, Random Forest, Gradient Boosting, XGBoost
LGD Model	Gradient Boosting with logit-transformed beta-distributed target
EAD Model	Credit Conversion Factor (CCF) approach per Basel framework
Credit Scorecard	WoE/IV-based scorecard with PDO scaling (300-850 range)
Model Validation	ROC/AUC, KS, Gini, PSI, calibration, lift, gains charts
Stress Testing	4 macroeconomic scenarios (baseline through deep depression)
Regulatory Capital	Basel III IRB capital formula with Vasicek ASRF model
Thesis PDF	Auto-generated 20-page thesis with embedded charts

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Project Structure

credit-risk-model/
├── main.py                      # Run the complete pipeline
├── generate_thesis_pdf.py       # Generate thesis/documentation PDF
├── config.py                    # Configuration & hyperparameters
├── requirements.txt             # Python dependencies
├── README.md
├── src/
│   ├── __init__.py
│   ├── data_generator.py        # Synthetic data with latent variable model
│   ├── eda.py                   # Exploratory Data Analysis & plots
│   ├── feature_engineering.py   # Feature pipeline (scaling, encoding, transforms)
│   ├── pd_model.py              # PD model suite (4 algorithms)
│   ├── lgd_model.py             # Loss Given Default model
│   ├── ead_model.py             # Exposure at Default / CCF model
│   ├── scorecard.py             # WoE credit scorecard
│   ├── validation.py            # 9-panel validation dashboard
│   ├── stress_testing.py        # Macroeconomic stress scenarios
│   └── capital.py               # Basel III IRB capital calculator
├── output/
│   ├── figures/                 # All generated charts (15+ PNGs)
│   ├── models/                  # Model comparison results
│   └── data/                    # Dataset & summary statistics
└── docs/
    └── Credit_Risk_Modeling_Thesis.pdf

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Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Run the Full Pipeline

python main.py

This single command executes all 10 stages and generates:

• 15+ publication-quality figures in output/figures/
• Model results in output/models/
• Complete dataset in output/data/
• 20-page thesis PDF in docs/

3. Generate Only the Thesis PDF

python generate_thesis_pdf.py

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Pipeline Stages

Stage 1: Data Generation

Generates 50,000 synthetic loan records using a latent variable model where default probability is driven by:

• Borrower features: FICO score, income, employment length, home ownership
• Credit history: utilization, delinquencies, inquiries, public records
• Loan terms: amount, rate, term, purpose, DTI ratio
• Macro variables: GDP growth, unemployment, fed funds rate, house price index

Stage 2: Exploratory Data Analysis

Produces diagnostic visualizations including:

• Default rate distribution (count & percentage)
• Feature distributions segmented by default status
• Correlation heatmap of 20+ features
• FICO score analysis with default rate by score band
• Macroeconomic sensitivity analysis
• Loan purpose default rate comparison

Stage 3: Feature Engineering

Creates 15+ derived features:

• Ratios: loan-to-income, balance-to-income, payment-to-income
• Interactions: FICO x utilization, FICO x DTI
• Transforms: log(income), log(loan), log(balance)
• Indicators: high utilization flag, delinquency flag
• Economic: real interest rate (nominal - GDP growth)

Stage 4: PD Model Training

Trains and compares four models with 5-fold stratified cross-validation:

Model	Key Hyperparameters
Logistic Regression	C=0.1, L2, balanced weights
Random Forest	300 trees, depth=8, leaf=50
Gradient Boosting	200 trees, lr=0.05, depth=4
XGBoost	300 trees, lr=0.05, L1+L2 reg

Stage 5: Model Validation

Nine-panel validation dashboard covering:

• ROC curve (train vs test)
• Precision-Recall curve
• Kolmogorov-Smirnov chart
• Calibration plot
• Score distribution by class
• Confusion matrix (at optimal threshold)
• Cumulative gains chart
• Lift chart (by decile)
• Population Stability Index (PSI)

Stage 6: Credit Scorecard

WoE-based scorecard with:

• Information Value (IV) ranking of all features
• Points-to-Double-Odds (PDO) scaling: base 600, PDO=20
• Rating grades: AAA through D
• Score range: 300-850

Stage 7-8: LGD & EAD Models

• LGD: Gradient Boosting with logit-transformed target on defaulted loans
• EAD: Credit Conversion Factor model: EAD = Drawn + CCF x (Limit - Drawn)

Stage 9: Stress Testing

Four macroeconomic scenarios with second-order effects:

Scenario	GDP Shock	Unemployment	Rate Shock
Baseline	0%	0%	0%
Mild Recession	-2%	+3%	+1%
Severe Recession	-5%	+7%	+2.5%
Deep Depression	-10%	+12%	+4%

Stage 10: Basel III Capital

IRB capital calculation using:

• Vasicek asymptotic single-risk-factor (ASRF) model
• 99.9% confidence level
• Asset correlation function: R(PD)
• Maturity adjustment: 2.5 years
• Capital components: CET1 (4.5%), Tier 1 (6%), Total (8%), Conservation Buffer (2.5%)

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Key Equations

Expected Loss:

EL = PD x LGD x EAD

Basel IRB Capital:

Conditional PD = Phi[(Phi^-1(PD) + sqrt(R) * Phi^-1(0.999)) / sqrt(1-R)]
K = [LGD * Conditional_PD - PD * LGD] * Maturity_Adjustment
RWA = K * 12.5 * EAD

Credit Score:

Score = Offset + Factor * ln((1-PD)/PD)
Factor = PDO / ln(2)
Offset = BaseScore - Factor * ln(TargetOdds)

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Output Files

File	Description
output/figures/01_default_distribution.png	Target variable analysis
output/figures/02_feature_distributions.png	Feature distributions by default
output/figures/03_correlation_matrix.png	Feature correlation heatmap
output/figures/04_fico_analysis.png	FICO score deep-dive
output/figures/05_macro_impact.png	Macroeconomic sensitivity
output/figures/06_loan_purpose.png	Purpose-based analysis
output/figures/07_model_comparison.png	PD model CV comparison
output/figures/08_feature_importance.png	RF & XGBoost importances
output/figures/09_validation_dashboard.png	9-panel validation suite
output/figures/10_decile_analysis.png	Risk decile calibration
output/figures/11_scorecard.png	Credit scorecard results
output/figures/12_lgd_analysis.png	LGD model diagnostics
output/figures/13_ead_analysis.png	EAD/CCF model results
output/figures/14_stress_testing.png	Stress test scenarios
output/figures/15_capital_analysis.png	Basel III capital breakdown
output/all_results.json	All numerical results
docs/Credit_Risk_Modeling_Thesis.pdf	Full thesis document

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Dependencies

• Python 3.9+
• numpy, pandas, scipy, statsmodels
• scikit-learn, xgboost
• matplotlib, seaborn
• reportlab, Pillow

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References

1. Merton, R.C. (1974). On the pricing of corporate debt. Journal of Finance.
2. Basel Committee (2006). International Convergence of Capital Measurement.
3. Basel Committee (2010). Basel III: Global regulatory framework.
4. Vasicek, O.A. (2002). The distribution of loan portfolio value. Risk.
5. Chen, T. & Guestrin, C. (2016). XGBoost. KDD.
6. Altman, E.I. (1968). Financial ratios and corporate bankruptcy. Journal of Finance.
7. Thomas, L.C. (2009). Consumer Credit Models. Oxford University Press.

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License

This project is for educational and research purposes.