The steadily high demand for cash contributes to the expansion of the network of Bank payment terminals. To optimize the amount of cash in payment terminals, it is necessary to minimize the cost of servicing them and ensure that there are no excess funds in the network. The purpose of this work is to create a cash management system in the network of payment terminals. The article discusses the solution to the problem of determining the optimal amount of funds to be loaded into the terminals, and the effective frequency of collection, which allows to get additional income by investing the released funds. The paper presents the results of predicting daily cash withdrawals at ATMs using a triple exponential smoothing model, a recurrent neural network with long short-term memory, and a model of singular spectrum analysis. These forecasting models allowed us to obtain a sufficient level of correct forecasts with good accuracy and completeness. The results of forecasting cash withdrawals were used to build a discrete optimal control model, which was used to develop an optimal schedule for adding funds to the payment terminal. It is proved that the efficiency and reliability of the proposed model is higher than that of the classical Baumol-Tobin inventory management model: when tested on the time series of three ATMs, the discrete optimal control model did not allow exhaustion of funds and allowed to earn on average 30% more than the classical model.
翻译:对现金的持续高需求有助于扩大银行支付终端网络。为了优化支付终端中的现金数量,有必要最大限度地降低服务这些终端的费用,并确保网络中没有多余的资金。这项工作的目的是在支付终端网络中建立一个现金管理系统。文章讨论了如何解决如何确定资金的最佳数额,以便把资金输入终端,以及有效的收取频率,从而通过投资发放的资金获得额外收入。文件介绍了使用三倍指数式平滑模型预测自动取款机每天提款的结果、一个具有长期记忆的经常性神经网络和单一频谱分析模型。这些预测模型使我们能够在支付终端网络中建立足够程度的准确和完整的正确预测。预测现金提取结果被用来建立一个离散的最佳控制模型,用于制定最佳时间表,将资金加入支付终端。事实证明,拟议模型的效率和可靠性高于典型的鲍莫利托宾库存模型的效率和可靠性:在对三个最高级的A-RMMM模型进行测试时,没有允许使用最高级的A-RMMMA模型。