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A stochastic programming approach to cash management in banking

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  • Castro, Jordi
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    Abstract

    The treasurer of a bank is responsible for the cash management of several banking activities. In this work, we focus on two of them: cash management in automatic teller machines (ATMs), and in the compensation of credit card transactions. In both cases a decision must be taken according to a future customers demand, which is uncertain. From historical data we can obtain a discrete probability distribution of this demand, which allows the application of stochastic programming techniques. We present stochastic programming models for each problem. Two short-term and one mid-term models are presented for ATMs. The short-term model with fixed costs results in an integer problem which is solved by a fast (i.e. linear running time) algorithm. The short-term model with fixed and staircase costs is solved through its MILP equivalent deterministic formulation. The mid-term model with fixed and staircase costs gives rise to a multi-stage stochastic problem, which is also solved by its MILP deterministic equivalent. The model for compensation of credit card transactions results in a closed form solution. The optimal solutions of those models are the best decisions to be taken by the bank, and provide the basis for a decision support system.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 192 (2009)
    Issue (Month): 3 (February)
    Pages: 963-974

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    Handle: RePEc:eee:ejores:v:192:y:2009:i:3:p:963-974

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    Web page: http://www.elsevier.com/locate/eor

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    Keywords: Stochastic programming OR in banking Integer stochastic programming;

    References

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    1. J. G. Kallberg & R. W. White & W. T. Ziemba, 1982. "Short Term Financial Planning under Uncertainty," Management Science, INFORMS, INFORMS, vol. 28(6), pages 670-682, June.
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    Cited by:
    1. V. Kamini & V. Ravi & A. Prinzie & D. Van Den Poel, 2013. "Cash Demand Forecasting in ATMs by Clustering and Neural Networks," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium, Ghent University, Faculty of Economics and Business Administration 13/865, Ghent University, Faculty of Economics and Business Administration.
    2. Venkatesh, Kamini & Ravi, Vadlamani & Prinzie, Anita & Poel, Dirk Van den, 2014. "Cash demand forecasting in ATMs by clustering and neural networks," European Journal of Operational Research, Elsevier, Elsevier, vol. 232(2), pages 383-392.
    3. Wong, Man Hong, 2013. "Investment models based on clustered scenario trees," European Journal of Operational Research, Elsevier, Elsevier, vol. 227(2), pages 314-324.
    4. Alaeddine Faleh, 2011. "Un modèle de programmation stochastique pour l'allocation stratégique d'actifs d'un régime de retraite partiellement provisionné," Working Papers hal-00561965, HAL.

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