A Methodology to Improve Cash Demand Forecasting for ATM Network

Darwish, Saad M.

doi:10.7763/ijcee.2013.v5.741

Cited by 20 publications

(17 citation statements)

References 11 publications

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“…Under the Inventory management view, in (Naserabadi et al, 2014), an approach for an inventory system is developed. Other approaches on ATM forecasting techniques are in (Darwish, 2013), where a brief summary of the existing methods for cash forecasting are presented.…”

Section: Literature Reviewmentioning

confidence: 99%

Money Leaks in Banking ATM’s Cash-Management Systems

Cabello

2020

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Some widely-accepted practices on banking ATM networks may negatively affect an efficient liquidity management. This paper analyses ATM cash management in light of empirical evidence which suggests that banking ATMs tend to be overloaded beyond the customer’s needs. This, in turn, results in high opportunity costs. While this is not perceived by banks as particularly harmful, it might have a damaging impact on other business which revolves exclusively around ATM networks, such as cashback sites. A dormant money case may be solved by an appropriate tool matching the ATM’s cash to the user’s needs. Supported by a large database of banking records, this paper also provides model validation for a set of theorems previously developed by the author, resulting here in a cutting-edge, reliable forecasting system, suitable for anticipating ATMs cash demand as well as coupling with other supply chain planning processes.

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Section: Literature Reviewmentioning

confidence: 99%

Money Leaks in Banking ATM’s Cash-Management Systems

Cabello

2020

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“…ATM is a computerized telecommunication device that provides a financial institution's customers a method of financial transactions in a public space without the need for a human clerk [2]. Most ATMs are connected to international bank networks, enabling people to withdraw and deposit money from machines not belonging to the bank or country where they have their account.…”

Section: Introductionmentioning

confidence: 99%

“…Using ATM cash management optimization and efficient cash loads routing, banks can avoid of stuck ATMs with cash and manage the system in dynamically changing environment by achievement the different requirements of ATM network participants. Recently, more banks are turning their attention to derive greater efficiency in how they manage their cash at ATMs [2].The key to the ATM's forecasting algorithms is to capture and process the historical data such that it provides insight into the future. Newly, some authors attempted to optimize the cash by modeling and forecasting the demand [3].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks

Bhandari¹,

Gill²

2016

IJCA

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Automatic teller machine (ATM) is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Accurate ATM forecasting for the future is one of the most important attributes to forecast because business sector, daily needs of people are highly largely dependent on this. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. Every financial institution (large or small) faces the same daily challenge. While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand. In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. In this research article an integrated BP/GA technique is proposed for accurate ATM forecasting. The results are very encouraging. The comparison of proposed technique with the previous one clarifies that the proposed model outperforms the previous models.

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“…However Simutis, Dilijonas and Bastina (2008) show that application of support vector machines to cash demand forecasting process has no superiority to neural networks and is even less accurate when reasonably long historical data period for model training is available. As an alternative to neural network approach, interval type-2 fuzzy neural network (IT2FNN) was applied for cash demand forecasting (Darwish 2013). This type of model has both on-line structure and parameter learning abilities that lets model automatically adapt to different cash flow processes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of ATM Cash Demand Process Factors Applied for Forecasting with CI Models

Zylius

2015

CJFA

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The purpose of cash management is to optimize distribution of cash. Effective cash management brings savings to retail banks that are related to: dormant cash reduction; reduced replenishment costs; decrease of cash preparation costs; reduction of cash insurance costs. Optimization of cash distribution for retail banking in ATM and branch networks requires estimation of cash demand/supply in the future. This estimation determines overall cash management efficiency: accurate cash demand estimation reduces bank overall costs. In order to estimate cash demand in the future, cash flow forecasting must be performed that is usually based on historical cash point (ATM or branch) cash flow data. Many factors that are uncertain and may change in time influence cash supply/demand process for cash point. These may change throughout cash points and are related to location, climate, holiday, celebration day and special event (such as salary days and sale of nearby supermarket) factors. Some factors affect cash demand periodically. Periodical factors form various seasonality in cash flow process: daily (related to intraday factors throughout the day), weekly (mostly related to weekend effects), monthly Gediminas Žylius 212 (related to payday) and yearly (related to climate seasons, tourist and student arrivals, periodical celebration days such as New Year) seasons. Uncertain (aperiodic) factors are mostly related to celebration days that do not occur periodically (such as Easter), structural break factors that form long term or permanent cash flow shift (new shopping mall near cash point, shift of working hours) and some may be temporal (reconstruction of nearby building that restricts cash point reachability). Those factors form cash flow process that contains linear or nonlinear trend, mixtures of various seasonal components (intraday, weekly, monthly yearly), level shifts and heteroscedastic uncertainty. So historical data-based forecasting models need to be able to approximate historical cash demand process as accurately as possible properly evaluating these factors and perform forecasting of cash flow in the future based on estimated empirical relationship.

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A Methodology to Improve Cash Demand Forecasting for ATM Network

Cited by 20 publications

References 11 publications

Money Leaks in Banking ATM’s Cash-Management Systems

Money Leaks in Banking ATM’s Cash-Management Systems

An Artificial Intelligence ATM forecasting system for Hybrid Neural Networks

Evaluation of ATM Cash Demand Process Factors Applied for Forecasting with CI Models

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