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Using Payment Behaviour Data for Credit Risk Modelling

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  • Nicholas Wilson
  • Barbara Summers
  • Robert Hope

Abstract

In this paper we evaluate payment scores in two contexts; that of predicting future payment behaviour and that of corporate failure prediction. The assessment of the ability and willingness of a firm to pay its creditors, and the likely timeliness of payments, are a major focus of both credit risk analysis (from the trade credit perspective) and government policy, although the latter issues have not been much studied in the academic literature. While failure prediction models are traditionally used as indicators of payment behaviour in the UK, payment behaviour prediction models are estimated and made available in the USA by the leading credit reporting agencies and the predictive abilities of such scores in the UK context are, therefore, worthy of consideration.We also consider the contribution that payment behaviour scores can make to predicting corporate failure. An important question is whether the availability of payment behaviour scores increases the overall information content of the credit report or merely re-packages information represented elsewhere. We find that payment behaviour data can be used to predict successfully future payment behaviour in a trade credit context, and can add incrementally to the predictivity of corporate failure models.

Suggested Citation

  • Nicholas Wilson & Barbara Summers & Robert Hope, 2000. "Using Payment Behaviour Data for Credit Risk Modelling," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 7(3), pages 333-346.
  • Handle: RePEc:taf:ijecbs:v:7:y:2000:i:3:p:333-346
    DOI: 10.1080/13571510050197230
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    References listed on IDEAS

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    1. Edward I. Altman, 1998. "Credit Risk Measurement and Management: The Ironic Challenge in the Next Decade," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-003, New York University, Leonard N. Stern School of Business-.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    3. Keasey, K & McGuinness, P & Short, H, 1990. "Multilogit approach to predicting corporate failure--Further analysis and the issue of signal consistency," Omega, Elsevier, vol. 18(1), pages 85-94.
    4. Beaver, Wh, 1968. "Market Prices, Financial Ratios, And Prediction Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 179-192.
    5. Lo, Andrew W., 1986. "Logit versus discriminant analysis : A specification test and application to corporate bankruptcies," Journal of Econometrics, Elsevier, vol. 31(2), pages 151-178, March.
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    Cited by:

    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Nicola Branzoli & Ilaria Supino, 2020. "FinTech credit: a critical review of empirical research," Questioni di Economia e Finanza (Occasional Papers) 549, Bank of Italy, Economic Research and International Relations Area.
    3. Jia-wen Zhang & Long-hui Chen & Xiang-yun Liu & Fen Ding, 2014. "Measurement of Credit Risk of Small and Medium-sized S&T Enterprises in China," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 5(4), pages 21-31, July.
    4. João Rebelo & José Vaz Caldas, 2010. "Default Mortgage Profile: A Micro Analysis Of The Portuguese Case," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(1), pages 109-125.
    5. 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.

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