Reference : Federated Learning for Credit Risk Assessment

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science Engineering, computing & technology : Multidisciplinary, general & others Business & economic sciences : Finance Business & economic sciences : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences; Finance
	To cite this reference:	http://hdl.handle.net/10993/54324

Title :	Federated Learning for Credit Risk Assessment
Language :	English
Author, co-author :	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 >]
Publication date :	3-Jan-2023
Main document title :	Proceedings of the 56th Hawaii International Conference on System Sciences
Pages :	10
Peer reviewed :	Yes
Audience :	International
ISBN :	978-0-9981331-6-4
Event name :	56th Hawaii International Conference on System Sciences
Event date :	from 03-01-23 to 06-01-23
Event organizer :	University of Hawaii
Event place (city) :	Maui, Hawaii
Event country :	USA
Keywords :	[en] artificial intelligence ; credit risk assessment ; federated learning ; financial collaboration
Abstract :	[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.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations ; University of Luxembourg: High Performance Computing - ULHPC
Funders :	European Commission - EC
Name of the research project :	Medical Device Obligations Taskforce
Target :	Researchers ; Professionals ; General public
Permalink :	http://hdl.handle.net/10993/54324
Other URL :	https://hdl.handle.net/10125/102676
Framework Programme / European Project :	H2020 ; 814654 - MDOT - Medical Device Obligations Taskforce
FnR project :	FnR ; FNR13342933 > Gilbert Fridgen > DFS > Paypal-fnr Pearl Chair In Digital Financial Services > 01/01/2020 > 31/12/2024 > 2019

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