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SAS Credit Risk Modelling- A to Z for PD Models: PD Modelling using SAS
Paperback
Series: Credit Risk Modelling Using SAS, Book 1
Publisher: Independently Published
Published: Aug 24 2025
Pages: 224
Weight: 0.67
Height: 0.47 Width: 6.00 Depth: 9.00
Language: English
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.
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