Credit Risk Modelling Using SAS A to Z for PD Models

Credit Risk Modelling - A to Z for PD Models (SAS Edition)Build a bank-grade Probability of Default (PD) model from scratch-using SAS, end to end. This first-of-its-kind, SAS-focused book takes you from raw banking data to a production-ready PD scorecard, with every step shown in executable code.What makes this book different- 100% SAS: Base SAS, PROC SQL, PROC LOGISTIC/HPLOGISTIC, plus practical macros used in real projects.- Hands-on, not just theory: every chapter includes working programs you can adapt immediately.- Regulatory alignment: maps modelling work to Basel expectations, IFRS 9, ICAAP/CCAR, documentation and governance.- Realistic data, safely: all examples use synthetic datasets designed to mirror real bank structures.- End-to-end lifecycle: ingestion → feature engineering → WoE/IV → logistic regression → score scaling → cutoffs/banding → deployment → monitoring/backtesting.- Case study you can run: a complete PD build that ties everything together.What you will learn- How to source and stitch core banking, bureau, delinquency (DPD) and collections data in SAS.- Robust feature engineering on wide, messy datasets (including how to tame duplicates and drift).- Weight of Evidence (WoE) binning and Information Value (IV) to select predictive variables the way banks do.- Building and explaining logistic regression PD models (odds, coefficients, diagnostics, KS/AUC/Lift).- Scaling scores, defining risk bands and business cutoffs.- Setting up monitoring with PSI, stability checks, challenger vs. champion, and recalibration triggers.- Packaging code for deployment and audit-ready documentation.Who it's for Risk analysts, SAS developers, model validators, data scientists and managers who want a clear, actionable path to a compliant PD model-without switching tools.What's included- Full SAS programs and a synthetic dataset via GitHub (safe for public use).- Reusable macros, templates and governance checklists.- Clear explanations written for practitioners, with just enough theory to justify decisions.If you build credit risk models in SAS-or need to validate, monitor and govern them-this book is your practical blueprint from zero to production.