IDEAS home Printed from https://ideas.repec.org/a/bla/jbfnac/v30y2003i9-10p1423-1435.html
   My bibliography  Save this article

Monitoring the Behaviour of Credit Card Holders with Graphical Chain Models

Author

Listed:
  • Elena Stanghellini

Abstract

Consumer credit has become an enormous business in industrialized countries. Recently, finance agencies have started to develop new products aiming not only to widen their portfolio but also to keep active relationships with good clients already taken on file and to prevent bad clients from becoming a loss for the agency. As a result, models for the whole behaviour of the clients are necessary. They involve many related outcome variables, which altogether give a measure of whether the client revealed to be profitable or unprofitable. This paper aims to show the potential of graphical chain models in the described context.

Suggested Citation

  • Elena Stanghellini, 2003. "Monitoring the Behaviour of Credit Card Holders with Graphical Chain Models," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(9‐10), pages 1423-1435, December.
    • Handle: RePEc:bla:jbfnac:v:30:y:2003:i:9-10:p:1423-1435
    DOI: 10.1111/j.0306-686X.2003.05451.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.0306-686X.2003.05451.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.0306-686X.2003.05451.x?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. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    2. E. Stanghellini & K. J. McConway & D. J. Hand, 1999. "A Discrete Variable Chain Graph for Applicants for Credit," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(2), pages 239-251.
    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. Nanny Wermuth & D.R. Cox, 1998. "On the Application of Conditional Independence to Ordinal Data," International Statistical Review, International Statistical Institute, vol. 66(2), pages 181-199, August.
    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. Ann Shawing Yang, 2015. "Lottery Payment Cards: A Study of Mental Accounting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(3), pages 201-226, July.

    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. 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).
    2. Elena Stanghellini, 2003. "Monitoring the Behaviour of Credit Card Holders with Graphical Chain Models," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(9‐10), pages 1423-1435, December.
    3. Thomas Wainwright, 2011. "Elite Knowledges: Framing Risk and the Geographies of Credit," Environment and Planning A, , vol. 43(3), pages 650-665, March.
    4. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    5. 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," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
    6. B. P. S. Murthi & Marina Girju & Erin Steffes, 2019. "The effect of promotional interest rates on customer borrowing and payment behavior in the credit card industry," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(1), pages 11-20, June.
    7. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.
    8. 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.
    9. Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    10. 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.
    11. Dawn Burton, 2012. "Credit Scoring, Risk, and Consumer Lendingscapes in Emerging Markets," Environment and Planning A, , vol. 44(1), pages 111-124, January.
    12. 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.
    13. Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.
    14. Yunus Emre Gür & Mesut Toğaçar & Bilal Solak, 2025. "Integration of CNN Models and Machine Learning Methods in Credit Score Classification: 2D Image Transformation and Feature Extraction," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2991-3035, May.
    15. Andreea Costea, 2017. "A Quantitative Approach to Credit Risk Management in the Underwriting Process for the Retail Portfolio," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 20(63), pages 157-186, March.
    16. Hand, David J., 2009. "Mining the past to determine the future: Problems and possibilities," International Journal of Forecasting, Elsevier, vol. 25(3), pages 441-451, July.
    17. Brad S. Trinkle & Amelia A. Baldwin, 2007. "Interpretable credit model development via artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(3‐4), pages 123-147, July.
    18. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    19. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
    20. Abdelkader Derbali & Lamia Jamel, 2019. "Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Case of Greek Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(2), pages 711-733, June.

    More about this item

    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:bla:jbfnac:v:30:y:2003:i:9-10:p:1423-1435. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0306-686X .

    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.