IDEAS home Printed from https://ideas.repec.org/h/zbw/entr20/224683.html
   My bibliography  Save this book chapter

A Review of Algorithms for Credit Risk Analysis

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Virtual Conference, 10-12 September 2020

Author

Listed:
  • Salihu, Armend
  • Shehu, Visar

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.

Suggested Citation

  • 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.
    • Handle: RePEc:zbw:entr20:224683
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/224683/1/14-ENT-2020-Salihu-134-146.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anu Bask & Hilkka Merisalo-Rantanen & Markku Tinnila & Theresa Lauraeus, 2011. "Towards e-banking: the evolution of business models in financial services," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 5(4), pages 333-356.
    2. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    3. 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.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    5. 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.
    6. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    7. 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.
    8. 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, vol. 9(1), pages 68-84, January.
    9. David Durand, 1941. "Risk Elements in Consumer Instalment Financing," NBER Books, National Bureau of Economic Research, Inc, number dura41-1, March.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    11. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    12. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    13. 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.
    14. Joris Van Gool & Wouter Verbeke & Piet Sercu & Bart Baesens, 2012. "Credit scoring for microfinance: is it worth it?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(2), pages 103-123, April.
    15. Kevin Curran & Jonathan Orr, 2011. "Integrating geolocation into electronic finance applications for additional security," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 5(3), pages 272-285.
    16. Baofeng Shi & Hufeng Yang & Jing Wang & Jingxu Zhao, 2016. "City Green Economy Evaluation: Empirical Evidence from 15 Sub-Provincial Cities in China," Sustainability, MDPI, vol. 8(6), pages 1-39, June.
    17. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    18. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    19. Pinches, George E & Mingo, Kent A, 1973. "A Multivariate Analysis of Industrial Bond Ratings," Journal of Finance, American Finance Association, vol. 28(1), pages 1-18, March.
    20. A.J. Feelders, 2000. "Credit scoring and reject inference with mixture models," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(1), pages 1-8, March.
    21. Dinh, Thi Huyen Thanh & Kleimeier, Stefanie, 2007. "A credit scoring model for Vietnam's retail banking market," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 471-495.
    22. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mydyti Hyrmet & Kadriu Arbana & Pejic Bach Mirjana, 2023. "Using Data Mining to Improve Decision-Making: Case Study of A Recommendation System Development," Organizacija, Sciendo, vol. 56(2), pages 138-154, May.

    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. 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.
    2. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    3. 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.
    4. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    5. Lobna Abid & Afif Masmoudi & Sonia Zouari-Ghorbel, 2018. "The Consumer Loan’s Payment Default Predictive Model: an Application of the Logistic Regression and the Discriminant Analysis in a Tunisian Commercial Bank," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 948-962, September.
    6. Mashhour Hathloul Maharmah & Esraa Ali Khalil, 2015. "Models Ability to Predict Bankruptcy Before it Happens," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(7), pages 479-491.
    7. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    8. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.
    9. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.
    10. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
    11. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
    12. Virág, Miklós & Nyitrai, Tamás, 2017. "Magyar vállalkozások felszámolásának előrejelzése pénzügyi mutatóik idősorai alapján [Predicting the liquidation of Hungarian firms using a time series of their financial ratios]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 305-324.
    13. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
    14. Olga V. Borisova & Olga A. Kalugina & Nikolay N. Kosarenko & Aleksandr V. Grinenko & Izida I. Ishmuradova, 2019. "Assessing the Financial Stability of Electric Power Organizations," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 66-76.
    15. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    16. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    17. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    18. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    19. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    20. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.

    More about this item

    Keywords

    Banking loan analysis; Classifiers; Credit risk analysis; Machine learning; Data mining;
    All these keywords.

    JEL classification:

    • 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
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

    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:zbw:entr20:224683. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://www.entrenova.org/ .

    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.