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The Formulation of Credit Policy Models

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  • Dileep Mehta

    (Graduate School of Business, Columbia University)

Abstract

This study undertakes to apply the statistical technique of sequential decision process to a specific range of problems of (trade) credit management. In particular, the study examines two problems: first, credit extension policy on a specific request or account; and second, construction of indices measuring the effectiveness of such a policy. The end goal is to establish a control system which has heretofore resisted analytical solution. Since management exercises its discretion primarily during the credit-extension phase, and since subsequent phases of credit policy are closely related to this particular phase, attention is focused on this aspect of credit policy. However, the analysis does not ignore other important aspects of credit policy, such as bad-debt level, length of the credit period, collection activities, and level of lost sales. The above situation is then reversed: indices in terms of bad-debt level, receivable level, etc., measure the impact of credit extension procedures on the subsequent phases of credit policy. The strength of the suggested approach lies in the logical relationship between the operating decision rules, that meaningfully take into account past experience, and the control indices. Thus it helps management in framing the optimal credit policy. The limitations of the study, such as the implied assumption of linearity of cost data and the neglect of cash discount policy or an integrated investment scheme, do not detract from the operational usefulness of the suggested approach.

Suggested Citation

  • Dileep Mehta, 1968. "The Formulation of Credit Policy Models," Management Science, INFORMS, vol. 15(2), pages 30-50, October.
  • Handle: RePEc:inm:ormnsc:v:15:y:1968:i:2:p:b30-b50
    DOI: 10.1287/mnsc.15.2.B30
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    Cited by:

    1. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    2. Du, Ruo & Banerjee, Avijit & Kim, Seung-Lae, 2013. "Coordination of two-echelon supply chains using wholesale price discount and credit option," International Journal of Production Economics, Elsevier, vol. 143(2), pages 327-334.
    3. Okumu Argan Wekesa & Mwalili Samuel & Mwita Peter, 2012. "Modelling Credit Risk for Personal Loans Using Product-Limit Estimator," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 3(1), pages 22-32, January.
    4. Seong Whan Shinn & Hwang, Hark & Sung, Soo Park, 1996. "Joint price and lot size determination under conditions of permissible delay in payments and quantity discounts for freight cost," European Journal of Operational Research, Elsevier, vol. 91(3), pages 528-542, June.
    5. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    6. Seifert, Daniel & Seifert, Ralf W. & Protopappa-Sieke, Margarita, 2013. "A review of trade credit literature: Opportunities for research in operations," European Journal of Operational Research, Elsevier, vol. 231(2), pages 245-256.
    7. Vandana, & Kaur, Arshinder, 2019. "Two-level trade credit with default risk in the supply chain under stochastic demand," Omega, Elsevier, vol. 88(C), pages 4-23.
    8. Smith, L. Douglas & Lawrence, Edward C., 1995. "Forecasting losses on a liquidating long-term loan portfolio," Journal of Banking & Finance, Elsevier, vol. 19(6), pages 959-985, September.
    9. Robert O. Edmister & Gay B. Hatfield, 1995. "The Significance of Porfolio Lenders to Real Estate Brokers," Journal of Real Estate Research, American Real Estate Society, vol. 10(1), pages 57-68.

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