Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications

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Автор: Iain Brown

Название: Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications

Язык: English

Издательство: Cary, NC: SAS Institute

Год: 2014

Формат: pdf

Размер: 11,7 mb

Страниц: 174

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.


About this Book

About the Author


Chapter 1 Introduction

1.1 Book Overview

1.2 Overview of Credit Risk Modeling

1.3 Regulatory Environment.

1.3.1 Minimum Capital Requirements.

1.3.2 Expected Loss

1.3.3 Unexpected Loss

1.3.4 Risk Weighted Assets

1.4 SAS Software Utilized

1.5 Chapter Summary

Chapter 2 Sampling and Data Pre-Processing

2.1 Introduction

2.2 Sampling and Variable Selection

2.2.1 Sampling

2.2.2 Variable Selection

2.3 Missing Values and Outlier Treatment

2.3.1 Missing Values

2.3.2 Outlier Detection

2.4 Data Segmentation

2.4.2 K-Means Clustering

2.5 Chapter Summary

2.6 References and Further Reading

Chapter 3 Development of a Probability of Default (PD) Model

3.1 Overview of Probability of Default

3.1.1 PD Models for Retail Credit

3.1.2 PD Models for Corporate Credit

3.1.3 PD Calibration

3.2 Classification Techniques for PD

3.2.1 Logistic Regression

3.2.2 Linear and Quadratic Discriminant Analysis

3.2.3 Neural Networks

3.2.4 Decision Trees

3.2.5 Memory Based Reasoning

3.2.6 Random Forests

3.2.7 Gradient Boosting

3.3 Model Development (Application Scorecards)

3.3.1 Motivation for Application Scorecards

3.3.2 Developing a PD Model for Application Scoring

3.4 Model Development (Behavioral Scoring)

3.4.1 Motivation for Behavioral Scorecards

3.4.2 Developing a PD Model for Behavioral Scoring

3.5 PD Model Reporting

3.5.1 Overview

3.5.2 Variable Worth Statistics

3.5.3 Scorecard Strength

3.5.4 Model Performance Measures

3.5.5 Tuning the Model

3.6 Model Deployment

3.6.1 Creating a Model Package

3.6.2 Registering a Model Package

3.7 Chapter Summary

3.8 References and Further Reading

Chapter 4 Development of a Loss Given Default (LGD) Model

4.1 Overview of Loss Given Default

4.1.1 LGD Models for Retail Credit

4.1.2 LGD Models for Corporate Credit

4.1.3 Economic Variables for LGD Estimation

4.1.4 Estimating Downturn LGD

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

4.3.1 Root Mean Squared Error

4.3.2 Mean Absolute Error

4.3.3 Area Under the Receiver Operating Curve

4.3.4 Area Over the Regression Error Characteristic Curves

4.3.5 R-square

4.3.6 Pearson’s Correlation Coefficient

4.3.7 Spearman’s Correlation Coefficient

4.3.8 Kendall’s Correlation Coefficient

4.4 Model Development

4.4.1 Motivation for LGD models

4.4.2 Developing an LGD Model

4.5 Case Study: Benchmarking Regression Algorithms for LGD

4.5.1 Data Set Characteristics

4.5.2 Experimental Set-Up

4.5.3 Results and Discussion

4.6 Chapter Summary

4.7 References and Further Reading

Chapter 5 Development of an Exposure at Default (EAD) Model.

5.1 Overview of Exposure at Default

5.2 Time Horizons for CCF

5.3 Data Preparation

5.4 CCF Distribution - Transformations

5.5 Model Development

5.5.1 Input Selection

5.5.2 Model Methodology

5.5.3 Performance Metrics

5.6 Model Validation and Reporting

5.6.1 Model Validation

5.6.2 Reports

5.7 Chapter Summary

5.8 References and Further Reading

Chapter 6 Stress Testing

6.1 Overview of Stress Testing

6.2 Purpose of Stress Testing

6.3 Stress Testing Methods

6.3.1 Sensitivity Testing

6.3.2 Scenario Testing

6.4 Regulatory Stress Testing

6.5 Chapter Summary

6.6 References and Further Reading

Chapter 7 Producing Model Reports

7.1 Surfacing Regulatory Reports

7.2 Model Validation

7.2.1 Model Performance

7.2.2 Model Stability

7.2.3 Model Calibration

7.3 SAS Model Manager Examples

7.3.1 Create a PD Report

7.3.2 Create a LGD Report

7.4 Chapter Summary

Tutorial A - Getting Started with SAS Enterprise Miner

A.1 Starting SAS Enterprise Miner

A.2 Assigning a Library Location

A. 3 Defining a New Data Set

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

Appendix A Data Used in This Book

A.1 Data Used in This Book

Chapter 3: Known Good Bad Data

Chapter 3: Rejected Candidates Data

Chapter 4: LGD Data

Chapter 5: Exposure at Default Data



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