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 ![]() | |
Delgado Fernandez, Joaquin ![]() | |
Potenciano Menci, Sergio ![]() | |
Rieger, Alexander ![]() | |
Fridgen, Gilbert ![]() | |
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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