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A Review of Algorithms for Credit Risk Analysis

Armend Salihu orcid.org/0000-0001-6602-2788 ; South East European University, North Macedonia
Visar Shehu ; South East European University, North Macedonia

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.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Keywords

Hrčak ID:

250945

URI

https://hrcak.srce.hr/250945

Publication date:

22.9.2020.

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