Brown - Developing credit risk models using SAS Enterprise Miner and SAS/STAT : theory and applications

Theory and Applications

Iain L. J. Brown, PhD

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The correct bibliographic citation for this manual is as follows: Brown, Iain. 2014. Developing Credit Risk Models Using SAS Enterprise Minerand SAS/STAT: Theory and Applications. Cary, NC: SAS Institute Inc.

Developing Credit Risk Models Using SASEnterprise Minerand SAS/STAT: Theory and Applications

Copyright 2014, SAS Institute Inc., Cary, NC, USA

ISBN 978-1-61290-691-1 (Hardcopy)
ISBN 978-1-62959-486-6 (EPUB)
ISBN 978-1-62959-487-3 (MOBI)
ISBN 978-1-62959-488-0 (PDF)

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December 2014

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This book sets out to empower readers with both theoretical and practical skills for developing credit risk models for Probability of Default (PD), Loss Given Default (LGD) and Exposure At Default (EAD) models using SAS Enterprise Miner and SAS/STAT. From data pre-processing and sampling, through segmentation analysis and model building and onto reporting and validation, this text aims to explain through theory and application how credit risk problems are formulated and solved.

Those who will benefit most from this book are practitioners (particularly analysts) and students wishing to develop their statistical and industry knowledge of the techniques required for modelling credit risk parameters. The step-by-step guide shows how models can be constructed through the use of SAS technology and demonstrates a best-practice approach to ensure accurate and timely decisions are made. Tutorials at the end of the book detail how to create projects in SAS Enterprise Miner and walk through a typical credit risk model building process.

In order to make the most of this text, a familiarity with statistical modelling is beneficial. This book also assumes a foundation level of SAS programming skills. Knowledge of SAS Enterprise Miner is not required, as detailed use cases will be given.

This book covers the use of SAS statistical programming (Base SAS, SAS/STAT, SAS Enterprise Guide), SAS Enterprise Miner in the development of credit risk models, and a small amount of SAS Model Manager for model monitoring and reporting.

This book does not provide proof of the statistical algorithms used. References and further readings to sources where readers can gain more information on these algorithms are given throughout this book.

SAS 9.4

SAS/STAT 12.3

SAS Enterprise Guide 6.1

SAS Enterprise Miner 12.3 (with Credit Scoring nodes)

SAS Model Manager 12.3

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SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across an enterprise. Data mining is applicable in a variety of industries and provides methodologies for such diverse business problems as fraud detection, customer retention and attrition, database marketing, market segmentation, risk analysis, affinity analysis, customer satisfaction, bankruptcy prediction, and portfolio analysis.