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Journal of the Operational Research Society Volume 73, 2022 - Issue 1: Special Issue on Credit Risk Modelling
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Original Articles

Transparency, auditability, and explainability of machine learning models in credit scoring

Michael Bückera FH Münster - University of Applied Sciences, Münster School of Business, Münster, GermanyCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-0045-8460
ORCID Icon,
Gero Szepannekb HOST – Stralsund University of Applied Sciences, Stralsund, GermanyORCID Iconhttps://orcid.org/0000-0001-8456-1283
ORCID Icon,
Alicja Gosiewskac Warsaw University of Technology, Warsaw, PolandORCID Iconhttps://orcid.org/0000-0001-6563-5742
ORCID Icon &
Przemyslaw Biecekc Warsaw University of Technology, Warsaw, Poland;d University of Warsaw, Warsaw, PolandORCID Iconhttps://orcid.org/0000-0001-8423-1823
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Pages 70-90 | Received 12 Jan 2020, Accepted 14 Apr 2021, Published online: 21 Jun 2021

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Rabin K. Jana, Indranil Ghosh & P. N. Ram Kumar. (2023) An explainable AI-enabled granular ensemble machine learning framework to demystify fertilizer price movements. Journal of the Operational Research Society 0:0, pages 1-18.
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Jonathan Crook, Tony Bellotti, Christophe Mues & Galina Andreeva. (2022) Editorial. Journal of the Operational Research Society 73:1, pages 1-4.
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Thanh Thuy Do, Golnoosh Babaei & Paolo Pagnottoni. Explainable Machine Learning for Credit Risk Management When Features are Dependent. Measurement: Interdisciplinary Research and Perspectives 0:0, pages 1-26.
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