IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v73y2021ics0038012119305440.html
   My bibliography  Save this article

Cluster Analysis for mixed data: An application to credit risk evaluation

Author

Listed:
  • Caruso, G.
  • Gattone, S.A.
  • Fortuna, F.
  • Di Battista, T.

Abstract

Credit risk is one of the main risks faced by a bank to provide financial products and services to clients. To evaluate the financial performance of clients, several scoring methodologies have been proposed, which are based mostly on quantitative indicators. This paper highlights the relevance of both quantitative and qualitative features of applicants and proposes a new methodology based on mixed data clustering techniques. Indeed, cluster analysis may prove particularly useful in the estimation of credit risk. Traditionally, clustering concentrates only on quantitative or qualitative data at a time; however, since credit applicants are characterized by mixed personal features, a cluster analysis specific for mixed data can lead to discover particularly informative patterns, estimating the risk associated with credit granting.

Suggested Citation

  • Caruso, G. & Gattone, S.A. & Fortuna, F. & Di Battista, T., 2021. "Cluster Analysis for mixed data: An application to credit risk evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:soceps:v:73:y:2021:i:c:s0038012119305440
    DOI: 10.1016/j.seps.2020.100850
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012119305440
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2020.100850?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Altman, Edward I. & Saunders, Anthony, 1997. "Credit risk measurement: Developments over the last 20 years," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1721-1742, December.
    2. Giulia Caruso & Stefano Antonio Gattone, 2019. "Waste Management Analysis in Developing Countries through Unsupervised Classification of Mixed Data," Social Sciences, MDPI, vol. 8(6), pages 1-15, June.
    3. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    4. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    5. Ai Cheo Yeo & Kate A. Smith & Robert J. Willis & Malcolm Brooks, 2001. "Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(1), pages 39-50, 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. Zhiwu Zhou & Julián Alcalá & Víctor Yepes, 2021. "Optimized Application of Sustainable Development Strategy in International Engineering Project Management," Mathematics, MDPI, vol. 9(14), pages 1-29, July.
    2. Nicolás Caselli & Ricardo Soto & Broderick Crawford & Sergio Valdivia & Rodrigo Olivares, 2021. "A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique," Mathematics, MDPI, vol. 9(16), pages 1-28, August.
    3. Ángel Acevedo-Duque & Romel Gonzalez-Diaz & Elena Cachicatari Vargas & Anherys Paz-Marcano & Sheyla Muller-Pérez & Guido Salazar-Sepúlveda & Giulia Caruso & Idiano D’Adamo, 2021. "Resilience, Leadership and Female Entrepreneurship within the Context of SMEs: Evidence from Latin America," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
    4. Michał Bernardelli & Zbigniew Korzeb & Paweł Niedziółka, 2022. "Does Fossil Fuel Financing Affect Banks’ ESG Ratings?," Energies, MDPI, vol. 15(4), pages 1-19, February.
    5. Jiang Hua & Tonglin Hao & Liangcai Zeng & Gui Yu, 2021. "YOLOMask, an Instance Segmentation Algorithm Based on Complementary Fusion Network," Mathematics, MDPI, vol. 9(15), pages 1-12, July.
    6. Lesly Lisset Ortiz-Cerezo & Alin Andrei Carsteanu & Julio Bernardo Clempner, 2022. "Sharpe-Ratio Portfolio in Controllable Markov Chains: Analytic and Algorithmic Approach for Second Order Cone Programming," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
    7. Veronica Piccialli & Antonio M. Sudoso & Angelika Wiegele, 2022. "SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2144-2162, July.
    8. Mohammad Motasem ALrfai & Danilah Binti Salleh & Waeibrorheem Waemustafa, 2022. "Empirical Examination of Credit Risk Determinant of Commercial Banks in Jordan," Risks, MDPI, vol. 10(4), pages 1-11, April.

    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. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    2. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    3. 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.
    4. Andreeva, Galina & Calabrese, Raffaella & Osmetti, Silvia Angela, 2016. "A comparative analysis of the UK and Italian small businesses using Generalised Extreme Value models," European Journal of Operational Research, Elsevier, vol. 249(2), pages 506-516.
    5. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Corazza, Marco & Funari, Stefania & Gusso, Riccardo, 2016. "Creditworthiness evaluation of Italian SMEs at the beginning of the 2007–2008 crisis: An MCDA approach," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 1-26.
    7. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2017. "PSO-based tuning of MURAME parameters for creditworthiness evaluation of Italian SMEs," Working Papers 04, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    8. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Journal of Entrepreneurial and Organizational Diversity, European Research Institute on Cooperative and Social Enterprises, vol. 3(1), pages 89-103, June.
    9. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    10. Francesco Ciampi, 2018. "Using Prior Payment Behavior Variables for Small Enterprise Default Prediction Modelling," International Journal of Business and Management, Canadian Center of Science and Education, vol. 13(4), pages 1-57, March.
    11. Joël Bessis, 2009. "Risk Management in Banking," Post-Print hal-00494876, HAL.
    12. K.K. Jain & P.K. Gupta & Sanjiv Mittal, 2011. "Logistic Predictive Model for SMEs Financing in India," Vision, , vol. 15(4), pages 331-346, December.
    13. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    14. Dinh, K. & Kleimeier, S., 2006. "Credit scoring for Vietnam's retail banking market : implementation and implications for transactional versus relationship lending," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    15. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    16. 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.
    17. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    18. Ryan Ball & Robert M. Bushman & Florin P. Vasvari, 2008. "The Debt‐Contracting Value of Accounting Information and Loan Syndicate Structure," Journal of Accounting Research, Wiley Blackwell, vol. 46(2), pages 247-287, May.
    19. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    20. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.

    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:eee:soceps:v:73:y:2021:i:c:s0038012119305440. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.