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Restructuring the credit process: behaviour scoring for german corporates

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  • Sebastian Fritz
  • Detlef Hosemann

Abstract

An instrument for automated monthly credit standing analysis based on data of the corporates current accounts is presented. Different methods of statistics and machine learning are used to develop scoring models for the supervision of debtors. The following methods were selected for model developement: Linear Discriminant Analysis Pattern Recognition (k‐nearest‐neighbours) Genetic Algorithms Neural Networks Decision Trees The developed models were compared not only concerning their classification results but also concerning score distribution, transparency and IT‐realisation. Copyright © 2000 John Wiley & Sons, Ltd.

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  • Sebastian Fritz & Detlef Hosemann, 2000. "Restructuring the credit process: behaviour scoring for german corporates," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(1), pages 9-21, March.
    • Handle: RePEc:wly:isacfm:v:9:y:2000:i:1:p:9-21
    DOI: 10.1002/(SICI)1099-1174(200003)9:13.0.CO;2-Q
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    1. Anders, Ulrich & Szczesny, Andrea, 1996. "Prognose von Insolvenzwahrscheinlichkeiten mit Hilfe logistischer neuronaler Netzwerke: Eine Untersuchung von kleinen und mittleren Unternehmen," ZEW Discussion Papers 96-27, ZEW - Leibniz Centre for European Economic Research.
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    Cited by:

    1. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    2. Daniel E. O'Leary, 2009. "Downloads and citations in Intelligent Systems in Accounting, Finance and Management," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 21-31, January.

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