Federated Learning for Credit Risk Assessment

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dc.contributor.authorLee, Chul Min
dc.contributor.authorDelgado Fernandez, Joaquin
dc.contributor.authorPotenciano Menci, Sergio
dc.contributor.authorRieger, Alexander
dc.contributor.authorFridgen, Gilbert
dc.date.accessioned2022-12-27T18:52:49Z
dc.date.available2022-12-27T18:52:49Z
dc.date.issued2023-01-03
dc.description.abstractCredit risk assessment is a standard procedure for financial institutions (FIs) when estimating their credit risk exposure. It involves the gathering and processing quantitative and qualitative datasets to estimate whether an individual or entity will be able to make future required payments. To ensure effective processing of this data, FIs increasingly use machine learning methods. Large FIs often have more powerful models as they can access larger datasets. In this paper, we present a Federated Learning prototype that allows smaller FIs to compete by training in a cooperative fashion a machine learning model which combines key data derived from several smaller datasets. We test our prototype on an historical mortgage dataset and empirically demonstrate the benefits of Federated Learning for smaller FIs. We conclude that smaller FIs can expect a significant performance increase in their credit risk assessment models by using collaborative machine learning.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.048
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.otherfc6192d4-b600-4668-b69b-bec2010d0be7
dc.identifier.urihttps://hdl.handle.net/10125/102676
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Science for Digital Collaboration
dc.subjectartificial intelligence
dc.subjectcredit risk assessment
dc.subjectfederated learning
dc.subjectfinancial collaboration
dc.titleFederated Learning for Credit Risk Assessment
dc.type.dcmitext
prism.startingpage386

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