Gaurav Chopra
A selection of credit-risk modelling work spanning regulatory frameworks, expected credit loss, scorecards, governance and probability-of-default estimation.
Credit risk · data science · model governance
Developed a Basel IRB modelling framework covering PD, LGD and EAD methodology, default definition, exposure estimation, collateral and recovery drivers, and CCF-based treatment for revolving facilities.
Mapped risk parameters to Expected Loss, Risk-Weighted Assets and regulatory capital calculations.
Developed ECL modelling frameworks using Flow Rate and State Transition Matrix approaches to analyse borrower movement across delinquency stages.
Supported PIT PD, LGD and EAD estimation with macroeconomic indicators and forward-looking scenario adjustments.
Built an end-to-end retail credit scorecard in Python using credit bureau attributes, including preprocessing, feature engineering and variable selection.
Developed a logistic regression scoring framework for borrower risk assessment, underwriting decisions and portfolio segmentation.
Designed a model governance and validation checklist covering pre-development validation, implementation controls and post-implementation monitoring.
Incorporated SR 11-7 and BCBS 239 guidance and defined First Line and Second Line governance responsibilities.
Compared Pluto–Tasche, Bayesian shrinkage, shock-adjusted and hybrid PD estimation approaches; incorporated Vasicek dependency modelling, scaling procedures and regulatory floors.