Credit risk analytics: measurement techniques, applications, and examples in SAS by Bart Baesens, Daniel Roesch, Harald Scheule.

Chapter 1: Introduction to Credit Risk Analytics; Why This Book Is Timely; The Current Regulatory Regime: Basel Regulations; Introduction to Our Data Sets; Housekeeping;
Chapter 2: Introduction to SAS Software; SAS versus Open Source Software; Base SAS; SAS/STAT; Macros in Base SAS; SAS Output Delivery System (ODS); SAS/IML; SAS Studio; SAS Enterprise Miner; Other SAS Solutions for Credit Risk Management; Reference;
Chapter 3: Exploratory Data Analysis; Introduction; One-Dimensional Analysis Two-Dimensional Analysis Highlights of Inductive Statistics; Reference;
Chapter 4: Data Preprocessing for Credit Risk Modeling; Types of Data Sources; Merging Data Sources; Sampling; Types of Data Elements; Visual Data Exploration and Exploratory Statistical Analysis; Descriptive Statistics; Missing Values; Outlier Detection and Treatment; Standardizing Data; Categorization; Weights of Evidence Coding; Variable Selection; Segmentation; Default Definition; Practice Questions; Notes; References;
Chapter 5: Credit Scoring; Basic Concepts; Judgmental versus Statistical Scoring Advantages of Statistical Credit ScoringTechniques to Build Scorecards; Credit Scoring for Retail Exposures; Reject Inference; Credit Scoring for Nonretail Exposures; Big Data for Credit Scoring; Overrides; Evaluating Scorecard Performance; Business Applications of Credit Scoring; Limitations; Practice Questions; References;
Chapter 6: Probabilities of Default (PD): Discrete-Time Hazard Models; Introduction; Discrete-Time Hazard Models; Which Model Should I Choose?; Fitting and Forecasting; Formation of Rating Classes; Practice Questions; References
Chapter 7: Probabilities of Default: Continuous-Time Hazard ModelsIntroduction; Censoring; Life Tables; Cox Proportional Hazards Models; Accelerated Failure Time Models; Extension: Mixture Cure Modeling; Discrete-Time Hazard versus Continuous-Time Hazard Models; Practice Questions; References;
Chapter 8: Low Default Portfolios; Introduction; Basic Concepts; Developing Predictive Models for Skewed Data Sets; Mapping to an External Rating Agency; Confidence Level Based Approach; Other Methods; LGD and EAD for Low Default Portfolios; Practice Questions; References
Chapter 9: Default Correlations and Credit Portfolio RiskIntroduction; Modeling Loss Distributions with Correlated Defaults; Estimating Correlations; Extensions; Practice Questions; References;
Chapter 10: Loss Given Default (LGD) and Recovery Rates; Introduction; Marginal LGD Models; PD-LGD Models; Extensions; Practice Questions; References;
Chapter 11: Exposure at Default (EAD) and Adverse Selection; Introduction; Regulatory Perspective on EAD; EAD Modeling; Practice Questions; References;
Chapter 12: Bayesian Methods for Credit Risk Modeling; Introduction.

The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management