15 citations · 23 across the 3 of their papers we have counts for
3 papers
stat.ML2020★ 15 cited
Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
Michael Bücker, Gero Szepannek, Alicja Gosiewska +1
A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus…
stat.CO2020★ 2 cited
An Overview on the Landscape of R Packages for Credit Scoring
Gero Szepannek
The credit scoring industry has a long tradition of using statistical tools for loan default probability prediction and domain specific standards have been established long before…
stat.ML2019★ 6 cited
How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models
Gero Szepannek
One of the most popular approaches to understanding feature effects of modern black box machine learning models are partial dependence plots (PDP). These plots are easy to understa…