Reference : Federated Learning for Credit Risk Assessment
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Engineering, computing & technology : Multidisciplinary, general & others
Business & economic sciences : Finance
Business & economic sciences : Multidisciplinary, general & others
Computational Sciences; Finance
http://hdl.handle.net/10993/54324
Federated Learning for Credit Risk Assessment
English
Lee, Chul Min mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
Delgado Fernandez, Joaquin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
Potenciano Menci, Sergio mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
Rieger, Alexander mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
Fridgen, Gilbert mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX >]
3-Jan-2023
Proceedings of the 56th Hawaii International Conference on System Sciences
10
Yes
International
978-0-9981331-6-4
56th Hawaii International Conference on System Sciences
from 03-01-23 to 06-01-23
University of Hawaii
Maui, Hawaii
USA
[en] artificial intelligence ; credit risk assessment ; federated learning ; financial collaboration
[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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations ; University of Luxembourg: High Performance Computing - ULHPC
European Commission - EC
Medical Device Obligations Taskforce
Researchers ; Professionals ; General public
http://hdl.handle.net/10993/54324
https://hdl.handle.net/10125/102676
H2020 ; 814654 - MDOT - Medical Device Obligations Taskforce
FnR ; FNR13342933 > Gilbert Fridgen > DFS > Paypal-fnr Pearl Chair In Digital Financial Services > 01/01/2020 > 31/12/2024 > 2019

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