Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions

This study conducts a benchmarking study, comparing 23 different statistical and machine learning methods in a credit scoring application. In order to do so, the models' performance is evaluated over four different data sets in combination with five data sampling strategies to tackle existing c...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Author: Stelzer, Anna
Format: Article
Language:English
Subjects:
Artificial intelligence
Data sampling
Machine learning
Performance prediction
Online Access:Get full text
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Summary:This study conducts a benchmarking study, comparing 23 different statistical and machine learning methods in a credit scoring application. In order to do so, the models' performance is evaluated over four different data sets in combination with five data sampling strategies to tackle existing class imbalances in the data. Six different performance measures are used to cover different aspects of predictive performance. The results indicate a strong superiority of ensemble methods and show that simple sampling strategies deliver better results than more sophisticated ones.
ISSN:2331-8422