Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/224683
Authors: 
Salihu, Armend
Shehu, Visar
Year of Publication: 
2020
Citation: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020 [ISSN:] 2706-4735 [Volume:] 6 [Pages:] 134-146
Abstract: 
The interest collected by the main borrowers is collected to pay back the principal borrowed from the depositary bank. In financial risk management, credit risk assessment is becoming a significant sector. For the credit risk assessment of client data sets, many credit risk analysis methods are used. The assessment of the credit risk datasets leads to the choice to cancel the customer's loan or to dismiss the customer's request is a challenging task involving a profound assessment of the information set or client information. In this paper, we survey diverse automatic credit risk analysis methods used for credit risk assessment. Data mining approach, as the most often used approach for credit risk analysis was described with the focus to various algorithms, such as neural networks.
Subjects: 
Banking loan analysis
Classifiers
Credit risk analysis
Machine learning
Data mining
JEL: 
G21
G32
H81
E51
Creative Commons License: 
https://creativecommons.org/licenses/by-nc/4.0/
Document Type: 
Conference Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.