A Review of Algorithms for Credit Risk Analysis

  • Armend Salihu South East European University, North Macedonia
  • Visar Shehu South East European University, North Macedonia
Keywords: Banking loan analysis, Classifiers, Credit risk analysis, Machine learning, Data mining


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.

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How to Cite
Salihu, A., & Shehu, V. (2020). A Review of Algorithms for Credit Risk Analysis. Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (Online), 6(1), 134-146. Retrieved from https://proceedings.entrenova.org/entrenova/article/view/317
Financial Economics