Book description
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.
Table of contents
- About this Book
- About the Author
- Acknowledgments
- Chapter 1 Introduction
- Chapter 2 Sampling and Data Pre-Processing
- Chapter 3 Development of a Probability of Default (PD) Model
-
Chapter 4 Development of a Loss Given Default (LGD) Model
- 4.1 Overview of Loss Given Default
-
4.2 Regression Techniques for LGD
- 4.2.1 Ordinary Least Squares – Linear Regression
- 4.2.2 Ordinary Least Squares with Beta Transformation
- 4.2.3 Beta Regression
- 4.2.4 Ordinary Least Squares with Box-Cox Transformation
- 4.2.5 Regression Trees
- 4.2.6 Artificial Neural Networks
- 4.2.7 Linear Regression and Non-linear Regression
- 4.2.8 Logistic Regression and Non-linear Regression
- 4.3 Performance Metrics for LGD
- 4.4 Model Development
- 4.5 Case Study: Benchmarking Regression Algorithms for LGD
- 4.6 Chapter Summary
- 4.7 References and Further Reading
- Chapter 5 Development of an Exposure at Default (EAD) Model
- Chapter 6 Stress Testing
- Chapter 7 Producing Model Reports
- Tutorial A – Getting Started with SAS Enterprise Miner
-
Tutorial B – Developing an Application Scorecard Model in SAS Enterprise Miner
-
B.1 Overview
- B.1.1 Step 1 – Import the XML Diagram
- B.1.2 Step 2 – Define the Data Source
- B.1.3 Step 3 – Visualize the Data
- B.1.4 Step 4 – Partition the Data
- B.1.5 Step 5 –Perform Screening and Grouping with Interactive Grouping
- B.1.6 Step 6 – Create a Scorecard and Fit a Logistic Regression Model
- B.1.7 Step 7 – Create a Rejected Data Source
- B.1.8 Step 8 – Perform Reject Inference and Create an Augmented Data Set
- B.1.9 Step 9 – Partition the Augmented Data Set into Training, Test and Validation Samples
- B.1.10 Step 10 – Perform Univariate Characteristic Screening and Grouping on the Augmented Data Set
- B.1.11 Step 11 – Fit a Logistic Regression Model and Score the Augmented Data Set
- B.2 Tutorial Summary
-
B.1 Overview
- Appendix A Data Used in This Book
Product information
- Title: Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT
- Author(s):
- Release date: December 2014
- Publisher(s): SAS Institute
- ISBN: 9781629594866
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