**By Bart Baesens, KU Leuven.** Sponsored Post.

**Credit risk analytics in R** will enable you to build credit risk models from start to finish. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing.

This book has been written as a companion to Baesens, B., Roesch, D. and Scheule H., Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS, John Wiley & Sons, 2016.

**Prof. dr. Bart Baesens** is a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has written more than 200 scientific papers and 10 books. His research is summarized atwww.dataminingapps.com

**Harry (Harald) Scheule **is Professor of Finance at the University of Technology Sydney. His expertise is in the area of Banking, Credit and Liquidity Risk, Data Analytics, Housing Finance, Insurance, Portfolio Construction, Prudential Regulation, and Securities Valuation. Harry is a strategic partner for banks and regulators who have applied his work to improve their risk management practices.

**Professor Dr. Daniel Rösch** holds the chair in Statistics and Risk Management at the University of Regensburg. His research interests cover Banking, Quantitative Financial Risk Management, Credit Risk, Asset Pricing, and Empirical Statistical and Econometric Methods and Models. He published numerous papers in leading international journals, earned several awards and honors, and regularly gives talks on major international conferences. Professor Rösch has cooperated with financial institutions and supervisory bodies such as Deutsche Bundesbank in joint research projects.