IDEAS home Printed from https://ideas.repec.org/a/mgt/youmgt/v18y2020i2p147-167.html

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
    as

    Download full text from publisher

    File URL: http://www.hippocampus.si/ISSN/1854-6935/18.147-167.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.26493/1854-6935.18.147-167?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Nabila Nisha, 2017. "An Empirical Study of the Balanced Scorecard Model: Evidence from Bangladesh," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global Scientific Publishing, vol. 9(1), pages 68-84, January.
    5. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1.
    6. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salihu, Armend & Shehu, Visar, 2020. "A Review of Algorithms for Credit Risk Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2020), Virtual Conference, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020, pages 134-146, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    2. Thomas Wainwright, 2011. "Elite Knowledges: Framing Risk and the Geographies of Credit," Environment and Planning A, , vol. 43(3), pages 650-665, March.
    3. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of a Tunisian Islamic Bank," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
    4. Victor Olkhov, 2020. "Price, Volatility and the Second-Order Economic Theory," Papers 2009.14278, arXiv.org, revised Apr 2021.
    5. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    6. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    7. Przemys{l}aw Biecek & Marcin Chlebus & Janusz Gajda & Alicja Gosiewska & Anna Kozak & Dominik Ogonowski & Jakub Sztachelski & Piotr Wojewnik, 2021. "Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models," Papers 2104.06735, arXiv.org.
    8. Eduard Sariev & Guido Germano, 2020. "Bayesian regularized artificial neural networks for the estimation of the probability of default," Quantitative Finance, Taylor & Francis Journals, vol. 20(2), pages 311-328, February.
    9. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    10. Büşra Alma Çallı & Erman Coşkun, 2021. "A Longitudinal Systematic Review of Credit Risk Assessment and Credit Default Predictors," SAGE Open, , vol. 11(4), pages 21582440211, November.
    11. Lili Li & Jun Yang & Xin Zou, 2016. "A study of credit risk of Chinese listed companies: ZPP versus KMV," Applied Economics, Taylor & Francis Journals, vol. 48(29), pages 2697-2710, June.
    12. George Bouchagiar, 2019. "The Long Road Toward Tracking the Trackers and De-biasing: A Consensus on Shaking the Black Box and Freeing From Bias," Review of European Studies, Canadian Center of Science and Education, vol. 11(1), pages 1-27, December.
    13. José Carlos Trejo-García & Miguel Ángel Martínez-García & Francisco Venegas-Martínez, 2017. "Administración del riesgo crediticio al menudeo en México: una mejora econométrica en la selección de variables y cambios en sus características," Contaduría y Administración, Accounting and Management, vol. 62(2), pages 11-12, Abril-Jun.
    14. Otima, Ruth M. A., 1994. "Predicting loan repayment performance: a case of Kenyan farm borrowers," ISU General Staff Papers 1994010108000018175, Iowa State University, Department of Economics.
    15. TOBBACK, Ellen & MARTENS, David, 2017. "Retail credit scoring using fine-grained payment data," Working Papers 2017011, University of Antwerp, Faculty of Business and Economics.
    16. Li, Huan & Wu, Weixing, 2024. "Loan default predictability with explainable machine learning," Finance Research Letters, Elsevier, vol. 60(C).
    17. Edgar R. Fiedler, 1971. "Section F: Bibliography," NBER Chapters, in: Measures of Credit Risk and Experience, pages 348-352, National Bureau of Economic Research, Inc.
    18. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 0. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 0, pages 1-11.
    19. Francesco Campanella, 2014. "Assess the Rating of SMEs by using Classification And Regression Trees (CART) with Qualitative Variables," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 16-32, August.
    20. Anjali Chopra & Priyanka Bhilare, 2018. "Application of Ensemble Models in Credit Scoring Models," Business Perspectives and Research, , vol. 6(2), pages 129-141, July.

    More about this item

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mgt:youmgt:v:18:y:2020:i:2:p:147-167. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alen Jezovnik (email available below). General contact details of provider: https://edirc.repec.org/data/fmkupsi.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.