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Transparency of Machine Learning Models in Credit Scoring

  • A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make “black box” machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.

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Author:Michael BückerORCiD, Gero Szepannek, Przemyslaw Biecek, Alicja Gosiewska, Mateusz Staniak
Parent Title (English):CRC Conference XVI Papers
Publisher:Credit Research Center, University of Edinburgh
Place of publication:Edinburgh
Editor:Jonathan Crook
Document Type:Conference Proceeding
Date of Publication (online):2019/10/16
Date of first Publication:2019/08/28
Provider of the Publication Server:FH Münster - University of Applied Sciences
Release Date:2019/10/17
First Page:1
Last Page:1
Faculties:Wirtschaft (MSB)
Publication list:Bücker, Michael
Licence (German):License LogoBibliographische Daten