Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS (Wiley and SAS Business Series) 1st Edition

I am impressed with the authors' coverage of credit risk modelling concepts and the analytics methodologies employed. For instance their presentation of analytics methodologies to handle the longitudinal nature of credit data is great. A well rounded discussion of discrete time hazard regression and survival analysis, among others, plus good coverage of selection issues in LGD analysis are provided. The provision of code and data enhances the book's appeal as a practical guide. They also provide a good list of references at the end of each chapter. This allows interested readers to widen their perspective by doing follow up reading.

This is a perfect book for person who works with statistical models. It describes theory as well as applied programs which permit to understand in a good way how the machine works. Explanations are clear and understandable by both experts of the sector (with link to research papers) and beginner.Charts and images permit to comprehend how SAS Enterprise Miner nodes work, with their complete results.Programs include good explanation and "step-by-step" comments which permit to be driven through the logic.

I consider this book to be very insightful in credit risk analytics, especially if you utilize SAS, although it is not necessary, the topics and content can be used by anyone involved with credit risk management. I recommend this book to all credit risk analysts.

interesting for students like me who want to develop their skills in credit risk using SAS Software

Very useful for my work. I think modeler need this

A very good book with program examples.

This book really helped me in implementing the models

Came with a broken spine, pages fell out immediately.

One of it's kind. A must for anyone serious about modelling credit risk.

An overview of credit rating process is really good. But some statistical models inside are impractical. Logistic regression scorecard development process is illustrated using reversed scaling. Writer suggest 100 corresponds to 50:1 (Bad:Good) odds and 120 corresponds to 100:1 odds, SAS suggested 600 corresponds to 1:30 (Bad:Good) and 620 corresponds to 1:60 (Bad:Good). It implies higher the score, lower the risk. Writers in this book suggest reverse scaling i.e. lower the score, lower the risk . However in subsequent topics they (in Exhibit:5.22) examples in scale: higher the score, lower the risk. Total confusion for the inexperience reader!

This text is incredibly thorough and provides the granular detail most alternative texts lack when it comes to Credit Risk Modeling. Its got great coverage as well. Baesens and co cover the spectrum of credit risk modeling from data analysis to model building (PD, LGD, EAD) and validation, stress testing etc. Datasets are available for download as well adding a nice practical hands-on element. I have found this text quite useful as a model validation practitioner and would argue that researchers and professionals at all levels would appreciate this well written text.

This is great book for building fundamentals on Credit risk parameters and Risk regulators. This is first source on Credit risk modelling I have seen which covers all basic concepts of credit risk and user can use it as a single reference point as compare to other books and online materials which provides a piecemeal knowledge.

A very good overview of credit risk modelling. A recommended book for a beginner. Would have liked the book to have some more in depth chapters. But overall a useful book to get started in credit risk modelling.