[en] Credit 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.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Gestion des systèmes d’information Finance Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres Sciences informatiques
Auteur, co-auteur :
LEE, Chul Min ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
DELGADO FERNANDEZ, Joaquin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
POTENCIANO MENCI, Sergio ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
RIEGER, Alexander ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
FRIDGEN, Gilbert ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Federated Learning for Credit Risk Assessment
Date de publication/diffusion :
03 janvier 2023
Nom de la manifestation :
Proceedings of the 56th Hawaii International Conference on System Sciences
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