Consumer credit risk: Individual probability estimates using machine learning

Jochen Kruppa, Alexandra Schwarz, Gerhard Arminger, Andreas Ziegler*

*Corresponding author for this work
79 Citations (Scopus)


Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the creditworthiness of consumers, and they are usually estimated by logistic regression. Here, we present a general framework for estimating individual consumer credit risks by use of machine learning methods. Since a probability is an expected value, all nonparametric regression approaches which are consistent for the mean are consistent for the probability estimation problem. Among others, random forests (RF), k-nearest neighbors (kNN), and bagged k-nearest neighbors (bNN) belong to this class of consistent nonparametric regression approaches. We apply the machine learning methods and an optimized logistic regression to a large dataset of complete payment histories of short-termed installment credits. We demonstrate probability estimation in Random Jungle, an RF package written in C++ with a generalized framework for fast tree growing, probability estimation, and classification. We also describe an algorithm for tuning the terminal node size for probability estimation. We demonstrate that regression RF outperforms the optimized logistic regression model, kNN, and bNN on the test data of the short-term installment credits.

Original languageEnglish
JournalExpert Systems with Applications
Issue number13
Pages (from-to)5125-5131
Number of pages7
Publication statusPublished - 2013


Dive into the research topics of 'Consumer credit risk: Individual probability estimates using machine learning'. Together they form a unique fingerprint.

Cite this