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Measurement, Monitoring, and Forecasting of Consumer Credit Default Risk - An Indicator Approach Based on Individual Payment Histories

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  • Alexandra Schwarz

    (German Institute for International Educational Research, Frankfurt am Main, Germany)

Abstract

The statistical techniques which cover the process of modeling and evaluating consumer credit risk have become widely accepted instruments in risk management. In contrast, we find only few and vague statements on how to define the default event, i. e. on the concrete circumstances that lead to the decision of identifying a certain credit as defaulted. Based on a unique data set of individual payment histories this paper proposes a definition of default which is based on the time due amounts are outstanding and the resulting profitability of the receivables portfolio. Furthermore, to assess the individual payment performance during the credit period, indicators for monitoring and forecasting default events are derived. The empirical results show that these indicators generate valuable information which can be used by the creditor to improve his credit and collection policy and hence, to improve cash flows and reduce bad debt loss.

Suggested Citation

  • Alexandra Schwarz, 2011. "Measurement, Monitoring, and Forecasting of Consumer Credit Default Risk - An Indicator Approach Based on Individual Payment Histories," Schumpeter Discussion Papers sdp11004, Universit├Ątsbibliothek Wuppertal, University Library.
  • Handle: RePEc:bwu:schdps:sdp11004
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    References listed on IDEAS

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    1. Dean P. Foster & Robert A. Stine, 2001. "Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy," Center for Financial Institutions Working Papers 01-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
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    Cited by:

    1. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.

    More about this item

    Keywords

    Credit Risk Analysis; Credit Default; Risk Management; Accounts Receivable Management; Performance Measurement;

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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