Adaptive Automated Teller Machines

Nowadays, Automated Teller Machines (ATMs) provide significant online support to bank customers. A limitation of ATM usage is that customers often have to wait in a queue, especially at ATMs installed at busy locations. Also, old people tend to consume more ATM usage time, possibly frustrating customers in the queue. In these situations, ATMs should ''adapt'' to the behavior of the customers to minimize the usage time. To this end, we apply data mining techniques to an ATM transaction dataset obtained from an international bank based in Kuwait. We pre-process this dataset, and convert it into a specific XML format to mine it through the ProM (process mining) tool. Our results reveal that customers withdraw money most frequently, followed by purchases (through an ATM card) and balance inquiry transactions. Customers re-do these transactions frequently, and also employ them one after the other. We acquire the distributions of the withdrawn amount, based on individual customers, the location (ATM terminal) and time of the withdrawl. Based on these results, we have proposed a set of five adaptive ATM interfaces, which show only frequent transactions and frequently-withdrawn amounts, display the current balance autonomously, and query explicitly for viewing purchase history, or for performing another withdrawl. An online survey on 216 ATM customers reveals that a majority of customers are willing to use these interfaces for minimizing their usage time. Our work has been approved by the banking authority of Pakistan, and we are currently implementing our interfaces for a Pakistani bank.