book

Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT

Name: Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT
Author: Ph. D Iain L. J. Brown
ISBN: 9781629594866

by Ph. D Iain L. J. Brown

December 2014

Beginner to intermediate

174 pages

5h 18m

English

SAS Institute

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About this Book
About the Author
Acknowledgments
Chapter 1 Introduction
1.1 Book Overview1.2 Overview of Credit Risk Modeling1.3 Regulatory Environment1.3.1 Minimum Capital Requirements1.3.2 Expected Loss1.3.3 Unexpected Loss1.3.4 Risk Weighted Assets1.4 SAS Software Utilized1.5 Chapter Summary1.6 References and Further Reading
Chapter 2 Sampling and Data Pre-Processing
2.1 Introduction2.2 Sampling and Variable Selection2.2.1 Sampling2.2.2 Variable Selection2.3 Missing Values and Outlier Treatment2.3.1 Missing Values2.3.2 Outlier Detection2.4 Data Segmentation2.4.1 Decision Trees for Segmentation2.4.2 K-Means Clustering2.5 Chapter Summary2.6 References and Further Reading
Chapter 3 Development of a Probability of Default (PD) Model
3.1 Overview of Probability of Default3.1.1 PD Models for Retail Credit3.1.2 PD Models for Corporate Credit3.1.3 PD Calibration3.2 Classification Techniques for PD3.2.1 Logistic Regression3.2.2 Linear and Quadratic Discriminant Analysis3.2.3 Neural Networks3.2.4 Decision Trees3.2.5 Memory Based Reasoning3.2.6 Random Forests3.2.7 Gradient Boosting3.3 Model Development (Application Scorecards)3.3.1 Motivation for Application Scorecards3.3.2 Developing a PD Model for Application Scoring3.4 Model Development (Behavioral Scoring)3.4.1 Motivation for Behavioral Scorecards3.4.2 Developing a PD Model for Behavioral Scoring3.5 PD Model Reporting3.5.1 Overview3.5.2 Variable Worth Statistics3.5.3 Scorecard Strength3.5.4 Model Performance Measures3.5.5 Tuning the Model3.6 Model Deployment3.6.1 Creating a Model Package3.6.2 Registering a Model Package3.7 Chapter Summary3.8 References and Further Reading
Chapter 4 Development of a Loss Given Default (LGD) Model
4.1 Overview of Loss Given Default4.1.1 LGD Models for Retail Credit4.1.2 LGD Models for Corporate Credit4.1.3 Economic Variables for LGD Estimation4.1.4 Estimating Downturn LGD4.2 Regression Techniques for LGD4.2.1 Ordinary Least Squares – Linear Regression4.2.2 Ordinary Least Squares with Beta Transformation4.2.3 Beta Regression4.2.4 Ordinary Least Squares with Box-Cox Transformation4.2.5 Regression Trees4.2.6 Artificial Neural Networks4.2.7 Linear Regression and Non-linear Regression4.2.8 Logistic Regression and Non-linear Regression4.3 Performance Metrics for LGD4.3.1 Root Mean Squared Error4.3.2 Mean Absolute Error4.3.3 Area Under the Receiver Operating Curve4.3.4 Area Over the Regression Error Characteristic Curves4.3.5 R-square4.3.6 Pearson’s Correlation Coefficient4.3.7 Spearman’s Correlation Coefficient4.3.8 Kendall’s Correlation Coefficient4.4 Model Development4.4.1 Motivation for LGD models4.4.2 Developing an LGD Model4.5 Case Study: Benchmarking Regression Algorithms for LGD4.5.1 Data Set Characteristics4.5.2 Experimental Set-Up4.5.3 Results and Discussion4.6 Chapter Summary4.7 References and Further Reading
Chapter 5 Development of an Exposure at Default (EAD) Model
5.1 Overview of Exposure at Default5.2 Time Horizons for CCF5.3 Data Preparation5.4 CCF Distribution – Transformations5.5 Model Development5.5.1 Input Selection5.5.2 Model Methodology5.5.3 Performance Metrics5.6 Model Validation and Reporting5.6.1 Model Validation5.6.2 Reports5.7 Chapter Summary5.8 References and Further Reading
Chapter 6 Stress Testing
6.1 Overview of Stress Testing6.2 Purpose of Stress Testing6.3 Stress Testing Methods6.3.1 Sensitivity Testing6.3.2 Scenario Testing6.4 Regulatory Stress Testing6.5 Chapter Summary6.6 References and Further Reading
Chapter 7 Producing Model Reports
7.1 Surfacing Regulatory Reports7.2 Model Validation7.2.1 Model Performance7.2.2 Model Stability7.2.3 Model Calibration7.3 SAS Model Manager Examples7.3.1 Create a PD Report7.3.2 Create a LGD Report7.4 Chapter Summary

Tutorial A – Getting Started with SAS Enterprise Miner
A.1 Starting SAS Enterprise MinerA.2 Assigning a Library LocationA.3 Defining a New Data Set
Tutorial B – Developing an Application Scorecard Model in SAS Enterprise Miner
B.1 OverviewB.1.1 Step 1 – Import the XML DiagramB.1.2 Step 2 – Define the Data SourceB.1.3 Step 3 – Visualize the DataB.1.4 Step 4 – Partition the DataB.1.5 Step 5 –Perform Screening and Grouping with Interactive GroupingB.1.6 Step 6 – Create a Scorecard and Fit a Logistic Regression ModelB.1.7 Step 7 – Create a Rejected Data SourceB.1.8 Step 8 – Perform Reject Inference and Create an Augmented Data SetB.1.9 Step 9 – Partition the Augmented Data Set into Training, Test and Validation SamplesB.1.10 Step 10 – Perform Univariate Characteristic Screening and Grouping on the Augmented Data SetB.1.11 Step 11 – Fit a Logistic Regression Model and Score the Augmented Data SetB.2 Tutorial Summary
Appendix A Data Used in This Book
A.1 Data Used in This BookChapter 3: Known Good Bad DataChapter 3: Rejected Candidates DataChapter 4: LGD DataChapter 5: Exposure at Default Data
Index

Overview

Combine complex concepts facing the financial sector with the software toolsets available to analysts.

The credit decisions you make are dependent on the data, models, and tools that you use to determine them. Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications combines both theoretical explanation and practical applications to define as well as demonstrate how you can build credit risk models using SAS Enterprise Miner and SAS/STAT and apply them into practice.

The ultimate goal of credit risk is to reduce losses through better and more reliable credit decisions that can be developed and deployed quickly. In this example-driven book, Dr. Brown breaks down the required modeling steps and details how this would be achieved through the implementation of SAS Enterprise Miner and SAS/STAT.

Users will solve real-world risk problems as well as comprehensively walk through model development while addressing key concepts in credit risk modeling. The book is aimed at credit risk analysts in retail banking, but its applications apply to risk modeling outside of the retail banking sphere. Those who would benefit from this book include credit risk analysts and managers alike, as well as analysts working in fraud, Basel compliancy, and marketing analytics. It is targeted for intermediate users with a specific business focus and some programming background is required.

Efficient and effective management of the entire credit risk model lifecycle process enables you to make better credit decisions. Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications demonstrates how practitioners can more accurately develop credit risk models as well as implement them in a timely fashion.

This book is part of the SAS Press Program.

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