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Credit risk estimation using payment history data: a comparative study of Turkish retail stores

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  • Mehmet Karan

    ()

  • Aydın Ulucan

    ()

  • Mustafa Kaya

    ()

Abstract

Our paper presents a comparative study applying logistic regression and multiple criteria decision analysis tools to the operations of wholesalers to assess the credit risk of their retailers using payment history data and to cluster the risky customers by ranking their risk levels. Our sample comprises approximately 6,000 retailer customers and 600.000 transactions of one of the major wholesalers of Turkey. Our findings emphasize the importance of using payment history and some non-financial factors data for predicting the creditworthiness of a firm. Copyright Springer-Verlag 2013

Suggested Citation

  • Mehmet Karan & Aydın Ulucan & Mustafa Kaya, 2013. "Credit risk estimation using payment history data: a comparative study of Turkish retail stores," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 21(2), pages 479-494, March.
  • Handle: RePEc:spr:cejnor:v:21:y:2013:i:2:p:479-494
    DOI: 10.1007/s10100-012-0242-y
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    References listed on IDEAS

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    Cited by:

    1. Vahid Baradaran & Maryam Keshavarz, 2017. "System dynamics modelling of retailers' credit risk," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 26(3), pages 380-396.

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