SAS Credit Risk Modelling- A to Z for PD Models: PD Modelling using SAS (CREDIT RISK MODELLING USING SAS) - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
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SAS Credit Risk Modelling- A to Z for PD Models: PD Modelling using SAS
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Paperback. Condition: new. Paperback. 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 different100% 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 learnHow 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 forRisk 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 includedFull 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798262654192
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SAS Credit Risk Modelling- A to Z for PD Models: PD Modelling using SAS (CREDIT RISK MODELLING USING SAS)
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Paperback. Condition: new. Paperback. 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 different100% 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 learnHow 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 forRisk 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 includedFull 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798262654192
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