Measurement, Monitoring, and Forecasting of Consumer Credit Default Risk - An Indicator Approach Based on Individual Payment Histories
AbstractThe 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.
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Bibliographic InfoPaper provided by Universitätsbibliothek Wuppertal, University Library in its series Schumpeter Discussion Papers with number sdp11004.
Date of creation: Apr 2011
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Web page: http://elpub.bib.uni-wuppertal.de
Credit Risk Analysis; Credit Default; Risk Management; Accounts Receivable Management; Performance Measurement;
Find related papers by 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 - - Business Economics - - - Business Economics
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-05-14 (All new papers)
- NEP-BAN-2011-05-14 (Banking)
- NEP-FOR-2011-05-14 (Forecasting)
- NEP-RMG-2011-05-14 (Risk Management)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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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