“Python Based End to End Integrated Credit Risk Modelling Framework Covering Development, Validation, Calibration & MRM Governance ”

 https://drive.google.com/drive/folders/1pERExuTBQM7n7v915OTAkRSia_RxKMrC?usp=drive_link

The developed Python framework implements an end-to-end credit risk modelling pipeline covering the complete lifecycle from data preparation to model monitoring and governance. The design aligns with model risk management expectations and ensures reproducibility, transparency, and regulatory compliance.

1. Data Preparation & Basic Controls

The process begins with loading historical credit risk data and defining the development sample window. Default definition is derived from rating thresholds, and time-based filtering is applied.

Key steps include:

• Date processing and feature alignment
• Default flag creation based on rating cut-off logic
• Aggregation of yearly default rates for trend analysis
• Missing value profiling and summary statistics generation
• Train-test split for development and validation stability checks

2. Variable Stability & Initial Screening

A structured variable filtering process is applied to ensure only stable and predictive factors are retained.

Techniques used:

• Population Stability Index (PSI) to assess distribution shift between train and test
• Kolmogorov-Smirnov (KS) test to evaluate discriminatory power
• Filtering based on PSI thresholds for stability
• Frequency distribution analysis across bins

3. Feature Binning & Predictive Strength

Variables are transformed into binned formats for interpretability and risk segmentation.

Steps include:

• Quantile-based binning of continuous variables
• Weight of Evidence (WOE) transformation
• Information Value (IV) calculation for predictive strength
• Selection of variables based on IV thresholds (moderate predictive power range)

4. Time Series & Statistical Validation

To ensure robustness and reduce spurious relationships:

• Augmented Dickey-Fuller (ADF) test is applied for stationarity
• Variables failing stationarity criteria are excluded from modelling

5. Multicollinearity & Dependency Checks

To avoid redundancy and unstable coefficients:

• Variance Inflation Factor (VIF) is computed
• Highly collinear variables are removed using predefined thresholds
• Partial correlation analysis is performed to further eliminate interdependent predictors

6. Logistic Regression Model Development

A stepwise logistic regression approach is implemented with constraints:

• Forward selection based on statistical significance (p-values)
• Business rule enforcement using expected coefficient signs
• Final model selection constrained by maximum parameter limit
• Model fitted using statsmodels Logit framework

7. Model Performance Evaluation

Comprehensive performance metrics are computed for both development and validation datasets:

• Accuracy, Precision, Recall, F1-score
• ROC-AUC and Gini coefficient
• KS statistic for separation power
• Log-loss for probabilistic performance

8. Calibration & Distribution Tests

Model calibration and statistical consistency are validated using:

• Hosmer–Lemeshow goodness-of-fit test
• Brier score for probability accuracy
• Jeffreys / binomial test for default consistency
• KS test between predicted and actual distributions

9. Rating System & Business Mapping

Model outputs are translated into interpretable risk grades:

• K-Means clustering on predicted PD values
• Mapping PD bands to rating grades (1–10 scale)
• Default classification based on rating thresholds
• Validation of monotonic relationship between rating and PD

10. Residual & Diagnostic Testing

Model assumptions are validated through:

• Durbin-Watson test for autocorrelation
• Breusch-Pagan test for heteroscedasticity
• Residual trend analysis

11. Cross-Validation & Robustness Checks

To ensure generalization:

• K-fold cross-validation using logistic regression
• Stability of accuracy, precision, recall, and F1-score evaluated across folds

12. Monitoring Framework (Out-of-Time Testing)

A monitoring module is implemented for post-deployment tracking:

• PSI computation on new (monitoring) data
• Drift detection across key variables
• Real-time performance tracking (AUC, KS, Gini, log-loss)
• Rating consistency checks

13. Governance & MRM Alignment

A structured Model Risk Management layer is embedded:

• Validation thresholds for key metrics (KS, Gini, PSI, calibration)
• Escalation rules for drift and performance deterioration
• Model lifecycle governance (development → validation → monitoring → recalibration)
• Alignment with SR 11-7 principles

Overall Summary

This framework consolidates credit risk model development, validation, and monitoring into a single automated pipeline. It integrates statistical rigor with governance requirements, enabling end-to-end model lifecycle management with audit-ready outputs.