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Data Mining Based Classifiers for Credit Risk Analysis

Author

Listed:
  • Armend Salihu

    (South East European University, North Macedonia)

  • Visar Shehu

    (South East European University, North Macedonia)

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.

Suggested Citation

  • Armend Salihu & Visar Shehu, 2020. "Data Mining Based Classifiers for Credit Risk Analysis," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 18(2 (Summer), pages 147-167.
    • Handle: RePEc:mgt:youmgt:v:18:y:2020:i:2:p:147-167
    DOI: 10.26493/1854-6935.18.147-167
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    References listed on IDEAS

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    1. Olafsson, Sigurdur & Li, Xiaonan & Wu, Shuning, 2008. "Operations research and data mining," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1429-1448, June.
    2. Emel, Ahmet Burak & Oral, Muhittin & Reisman, Arnold & Yolalan, Reha, 2003. "A credit scoring approach for the commercial banking sector," Socio-Economic Planning Sciences, Elsevier, vol. 37(2), pages 103-123, June.
    3. Mehmet Karan & Aydın Ulucan & Mustafa Kaya, 2013. "Credit risk estimation using payment history data: a comparative study of Turkish retail stores," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(2), pages 479-494, March.
    4. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1, March.
    5. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    banking loan analysis; classifiers; credit risk analysis; machine learning; data mining;
    All these keywords.

    JEL classification:

    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • H81 - Public Economics - - Miscellaneous Issues - - - Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts

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