Forecasting cash management, security, ease of use, and so on are important in the use of Automated Teller Machine (ATM). For this purpose, in this paper, we have discussed issues such as forecasting cash demand, fraud detection, ATM failure, user interface, replenishment strategy, ATM location, customer behavior, etc. Artificial Intelligence (AI) techniques are discussed for the detection of fraud, failure, replenishment and crash prediction. A number of statistical methods used to evaluate these forecasts are also covered in this paper. Moreover, we review AI techniques such as neural networks, regressions, support vector machines and their results in the form graphs in different sections. The literature covered in this paper is related to the past ten years (2006–2016). The approaches studied in this paper are compared in terms of data sets and prediction performance, accuracy and so on. We also provide a list of data sets available for the scientific community to conduct research in this field. Finally, open issues and future works in each of these items are presented.
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