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Michael Bücker

1 paper hereh-index 3225 citations10 works total

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author position
  • first author1

Across the 1 of 1 paper where every author was matched, so the position is known.

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  • stat.ML1

identity via Semantic Scholar / OpenAlex

most citedTransparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

15 citations · 15 across the 1 of their papers we have counts for

collaborators

1 paper

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.