"I can recommend Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications to anyone who would like to know more about credit risk modeling. It is a well-written and well-structured book for both practitioners and students.

"This book starts by providing a general overview on the Basel framework and then it introduces the three key parameters of Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD). The second chapter is about sampling and data preparation. What I like most about this chapter is that it shows how SAS Enterprise Miner could be used to facilitate the sampling and data pre-processing in such a simple and quick manner.

"The following three chapters are dedicated to the three key parameters of PD, LGD, and EAD, respectively. These chapters do not only discuss some popular and key modeling techniques, but they also provide details about some essential performance measures for model evaluation. These chapters have a good balance between the underlying mathematical models and some topics about applying those techniques in practice. For example, there are sections discussing PD model deployment, the estimation of downturn LGD, and the calculation of credit conversion factors in EAD models, etc. Stress testing is one of the topics highlighted by financial regulators. Chapter 6 certainly helps readers to explore different types of stress testing approaches.

"Overall, Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications provides a comprehensive coverage of credit risk modeling. If you are experienced in the area, this book provides insights for you to overcome some practical challenges. If you are new to credit risk modeling, this book provides solid materials to build your knowledge in this area."

Dr. Mee Chi So
Lecturer in Marketing
Program Leader MSc Marketing Analytics Southampton Business School

Iain Brown's book Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications is an essential book for risk analysts at all levels. Students of credit modeling and new professionals will find a complete and detailed roadmap to understanding, calculating, and implementing key risk parameters. Experienced analysts will turn to this book repeatedly for its exhaustive coverage of methodology and theory behind credit risk modeling. Regardless of proficiency, readers will be impressed by Dr. Brown's impressive literature review.

Dr. Brown presents a perfect blend of SAS technology and credit risk industry knowledge. In his book, he marries the three heavy weights of modeling, SAS Enterprise Miner, SAS/STAT, and SAS Model Manager showing readers multiple ways of accomplishing the same task. For example, he demonstrates how PROC SURVEYSELECT in SAS/STAT and the Sample node in SAS Enterprise Miner can accomplish the task of sampling the data. Each chapter is presented with an excellent blend of industry knowledge and recommendations, as well as how-to procedures to accomplish the calculation of key risk parameters.

I am confident that Dr. Brown's book will be an invaluable asset to risk analysts.

Renu Gehring
SAS Consultant

Author of Administrative Healthcare Data: A Guide to Its Origin, Content, and Application Using SAS and SAS Business Intelligence for the Health Care Industry: Practical Applications.
"Credit risk modelling continues to be an essential focus for banks, financial institutions, and regulatory agencies, in addition to active university research across the globe. Dr Brown has leveraged his peer-reviewed research findings in Basel modelling along with industry experience from banking and SAS to author a comprehensive text on developing credit risk models using SAS Enterprise Miner and SAS/STAT. Numerous approaches from both statistical and data mining methods are demonstrated for model development showing the flexibility of SAS in accommodating a wide range of advanced modelling techniques. Additionally, sampling methods, data pre-processing, and model validation metrics are discussed, which illustrate the full cycle of model development.

"This book is highly recommended for understanding how SAS can be used to develop effective scorecards and the suite of PD, LGD, and EAD risk parameter models for regulatory Basel compliance. These models have critical roles in banks' risk management and performance measurement processes, along with active portfolio management and capital planning decisions."

Edward Tong, PhD

"Must have it! This book is a tremendous resource and should be required reading for anyone who will be working with administrative healthcare claims data and SAS. Craig Dickstein and Renu Gehring have done a masterful job of providing a clear and well-written document about administrative healthcare claims within the context of the broader healthcare system. This book is filled with details about facility, professional, and pharmacy claim types and all the key coding systems that are used. Case studies of industry-wide hospital use, financial rates, HEDIS preventive care, and medication adherence measures are provided using SAS Enterprise Guide, SAS macros, and basic SAS coding techniques. New and experienced analysts will benefit significantly from Administrative Healthcare Data: A Guide to Its Organization, Content, and Application Using SAS."

Karl Finison
Director of Analytic Development

"Dr. Brown's book Developing Credit Risk Models Using SAS Enterprise Miner and SAS/STAT: Theory and Applications comprehensively describes the theory and application of development and validation of credit risk models using SAS. It provides a variety of methods on modelling each risk parameter (PD/LGD/EAD) and gives detailed illustrations of developing a risk model in each step from data preparation to model validation. This book is very suitable for new graduates who decide to develop a career in risk modelling and analytics, and it is also helpful for experienced analysts who want to enhance their risk modelling knowledge base. I believe this book will be a very useful tool for all levels of risk professionals."

Jie Zhang, PhD