Data Mining Based Classifiers for Credit Risk Analysis

Armend Salihu and Visar Shehu
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Armend Salihu: South East European University, North Macedonia
Visar Shehu: South East European University, North Macedonia

Managing Global Transitions, 2020, vol. 18, issue 2 (Summer), 147-167

Abstract: In order to pay back the principal borrowed from the depositary bank, the interest collected by principal creditors will be collected. In this paper, we have presented the main classifiers which are used in credit evaluation. From the research,we have noticed that there are some classifiers who find application in the credit allocation decision. Credit risk assessment is becoming a critical field of financial risk management. Many approaches are used for the credit risk evaluation of client data sets. The evaluation of credit risk data sets leads to an option of cancelling the loan or refusing the request of the borrower, which requires a detailed examination of the data set or of the customer’s data. This paper discusses various automatic methods of credit risk analysis used for the estimation of credit risk. The data mining method was defined with the emphasis on different algorithms, such as neural network, and as the most widely employed approach for credit risk analysis.

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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DOI: 10.26493/1854-6935.18.147-167

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