A Review of Algorithms for Credit Risk Analysis

Armend Salihu and Visar Shehu

A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, 2020, pp 134-146 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb

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.

Keywords: Banking loan analysis; Classifiers; Credit risk analysis; Machine learning; Data mining (search for similar items in EconPapers)
JEL-codes: E51 G21 G32 H81 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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