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The purpose of this paper is to introduce, model, and solve a rich multiperiod inventory-routing problem with pickups and deliveries motivated by the replenishment of automated teller machines in the Netherlands. Commodities can be brought to and from the depot, as well as being exchanged among customers to efficiently manage their inventory shortages and surpluses. A single customer can both provide and receive commodities at different periods, since its demand changes dynamically throughout the planning horizon and can be either positive or negative. In the case study, new technology provides these machines with the additional functionality of receiving deposits and reissuing banknotes to subsequent customers. We first formulate the problem as a very large-scale mixed-integer linear programming model. Given the size and complexity of the problem, we first decompose it into several more manageable subproblems by means of a clustering procedure, and we further simplify the subproblems by fixing some variables. The resulting subproblems are strengthened through the generation of valid inequalities and solved by branch and cut. We assess the performance of the proposed solution methodology through extensive computational experiments using real data. The results show that we are able to obtain good lower and upper bounds for this new and challenging practical problem.

This study was conducted to verify a system of Automated Teller Machine (ATM) which is a facility provided by a bank. Various transactions can be done by using ATM, including cash withdrawal, payment, and transfer. However, in spite of its function, ATM can also be a target of crime such as cash robbery and frauds. Therefore, the correctness of ATM and its security is essential and for that reason, formal verification is needed. Formal verification is a technique to ensure the model of a system to satisfy a certain specification. ATM has a time variable on its system. Therefore, timed automata can be used as a model of ATM. In this paper, an algorithm is constructed based on the ATM cash withdrawal steps. Next, we construct a timed automaton model and design nine specifications. Then, timed automaton were verified against these nine specifications using UPPAAL. From the verification results, it can be concluded that the security of the ATM system is guaranteed.

Introduction and objective: While the qualitative information about bacterial and fungal pollution of automated teller machine (ATM) surfaces is available in the scientific literature, there are practically no studies precisely quantifying this type of contamination. Regarding viruses, such data in relation to ATM surfaces are not available at all. Material and methods: The quantitative and qualitative control of adeno- and coronaviruses, including SARS-CoV-2 (based on qPCR/RT-qPCR and v-qPCR/v-RT-qPCR), bacterial and fungal contaminants (based on morphological and biochemical characteristics followed by PCR/RAPD typing) deposited on internal and external ATM surfaces (swab sampling), as well as present in the air of premises housing the ATM machines (inertial impaction sampling) belonging to the network of one of the largest Polish banks was performed. Results: As the air of premises housing ATMs was relatively clean, the internal (i.e. safe boxes and cash dispenser tracks) and external (i.e. touch screens and keypads) ATM surfaces were heavily polluted, reaching 599 CFU/cm2, 522 CFU/cm2, 17288 gc/cm2 and 2512 gc/cm2 for bacterial, fungal, coronaviral and adenoviral contaminants, respectively. The application of propidium monoazide (PMA) dye pretreatment for v-qPCR/v-RT-qPCR allows detection of the potentially infectious SARS-CoV-2 and adenoviral particulates on ATM surfaces. Conclusions: The packaged banknotes and people involved in their distribution, as well as general population using ATMs, can be the sources of this type of contamination and its potential victims. Highly efficient hygienic measures should be introduced to prevent unwanted pollution of both the distributed means of payment and ATM surfaces, and to avoid subsequent dissemination of microbial contaminants.

Purpose - The purpose of this paper is to propose and investigate the dimensions of automated teller machine (ATM) service quality and their relationship with customer satisfaction in the retail banking sector. Design/methodology/approach - A structured questionnaire gleaned from the literature was used to collect data from 530 ATM customers of 15 banks in Ghana. Descriptive statistics, confirmatory factor analysis were used to identify the dimensions of ATM service quality and their relationship with customer satisfaction. Findings - The study found convenience, reliability, ease of use, privacy and security, responsiveness and fulfillment to be the major dimensions of ATM service quality. Apart from security and privacy, these dimensions are significantly related to customer satisfaction. Practical implications - The ATM quality dimensions found in this study provide practical guidelines for bank managers to improve customer experience with ATMs. The relative importance of the factors identified in the study also provide managers with a sense of what issues to focus on in order to improve service delivery through the ATMs. Originality/value - The ATM service quality dimensions found in this study have enriched knowledge in electronic banking usage in developing countries such as Ghana. In addition, the study also provides bank managers with insights into how to improve customer satisfaction in retail banking through the usage of ATMs.

Over the past years, clients have largely depended on and trusted Automated Teller Machines (ATMs) to fulfill their banking needs and control their accounts easily and quickly. Despite the significant advantages of ATMs, fraud has become a very high risk and danger. As it leads to controlling all clients' accounts. In this paper, the proposed framework is using the iris recognition technology combined with the one-time password (OTP) to detect and prevent the known as well as the unknown attacks on ATMs and provide a table of the attackers and the suspected attackers with a counter to take a preventive action with them. Our proposed preventive actions are: card withdrawal, flagging the identified iris as an attacker in the database, notifying the card owner with this suspicious behavior, reporting to the Central Bank of Egypt (CBE), and calling the police when an attacker's iris counts three capturing times, even if for a different card. Two case studies were attempted to achieve the highest accuracy, the first case was using the Chinese Academy of Sciences' Institute of Automation V1.0 (CASIA-IrisV1) dataset using the Cosine Distance. The second one was using the Indian Institute of Technology Delhi (IITD) dataset using k-Nearest Neighbors (KNN) and Histogram of Oriented Gradient (HOG) techniques together reaching 100% accuracy.

During the last decades, the banking industry has been facing multiple challenges, from changes in regulations and legal systems, to the irruption of new technologies. A constant adaptation to the rapidly changing business ecosystem is therefore required. This paper deals with one type of efficiency improvement that is required from traditional banking operations as a result of the current shift in the industry towards more use of technology. That is, we deal with the decision-making process related to the redesign of existing ATM networks, which is currently being considered by many banks worldwide. This redesign is mainly a consequence of an increased use of electronic payment methods and card transactions, which have made the demand for cash decrease significantly. We describe a multi-objective mathematical programming model to be used as a tool when making strategic decisions regarding the bank ATM network redesign. Total costs and the coverage of customers and turnover are considered as conflicting performance attributes and analyzed through several multi-criteria techniques, namely the weighted sum and Tchebycheff methods and Archimedean goal programming. Among other insights, the results on a realistic case study show that the potential for cost reductions is high and that the consideration of both customer coverage and turnover coverage is crucial for the performance of the network.

The banking industry has been employing artificial intelligence (AI) technologies to enhance the quality of its services. More recently, AI algorithms, such as natural language understanding (NLU), have been integrated into chatbots to improve banking applications. These chatbots are typically designed to cater to customers’ needs. However, research in the development of troubleshooting chatbots for technical purposes remains scarce, especially in the banking sector. Although a company may possess a knowledge database, a standard methodology is essential to guiding an AI developer in building a chatbot, making the modeling of technical needs into a specialized chatbot a challenging task. This paper presents a novel methodology for developing troubleshooting chatbots. We apply this methodology to create an AI-powered chatbot capable of performing technical ATM maintenance tasks. We propose the TroubleshootingBot, an experimental protocol to obtain data for evaluating the chatbot through two scenarios. The first scenario detects user intent, and the second recognizes desired values in a user’s phrase (e.g., three beeps or two beeps). For these scenarios, we achieved accuracies of 0.93 and 0.88, respectively. This work represents a significant advancement in virtual assistants for banking applications and holds potential for other technical problem-solving applications.

PurposeBoth branch and automated teller machine (ATM) are playing a crucial role in banking coverage expansion in India. People prefer to go to an ATM for withdrawal of money rather waiting in a queue for hours at a branch. Without the existence of a full-fledged brick-and-mortar branch, ATM also plays an important role by providing basic banking services. In India, a significant part of the population is excluded from banking access. The present study aims to investigate how the branch and ATM penetration influence financial inclusion.Design/methodology/approachThe study covers the period from 2008–2009 to 2019–2020. With the application of Welch's t-test, a comparative study is being conducted between branch and ATM. Further, with the application of regression analysis, the study analyses how the branch and ATM network expansion influence financial inclusion.FindingsThough in recent times customers prefers to visit an ATM and its growth rate is higher than branches, the study found no significant differences between the growth of branch and ATM. Further, results of regression show both branches and ATMs have significant impacts on financial inclusion.Originality/valueIn micro concept both have a common role in respect of service provided to customers. While in macro concept a list of specific services can be provided through branch level only. This study has a significant role, considering the importance of branches or ATMs and cost of installing a physical branch.

The authors show that integrity protection as a technical means towards automated teller machine (ATM) security is not enough to establish trust towards ATM users. The attacks, aiming at getting into possession of users’ bank card details and personal identification numbers (PINs) are manifold. The authors come up with a solution that allows users to establish trust into the ATM integrity protection being in place. The users’ mobile phones play a central role in the trust establishment. The authors also shift the PIN entry away from the possibly insecure ATM's PIN pad towards the users’ mobile phones.

Covering face with accessories such as mask, scarf and sunglass is a common criminal activity in automated teller machine (ATM) robbery. Therefore, detection of covered face using ATM surveillance camera can be an effective solution to reduce robbery or crime. This paper presents a novel method to detect covered face from ATM surveillance camera images. Specifically, three facial features, i.e., skin color, elliptical face shape and facial width-to-height ratio (fWHR), incorporated with geometrical property of ellipse have been employed to estimate the covered region. In addition, three parameters, i.e., facial area, fWHR and covered area percentage, have been utilized for reliable classification. Experiment results demonstrate that the method can detect full covered, uncovered and partially covered faces at a correct detection rate of 98.3%, 93.3% and 97.78%, respectively. The overall correct detection rate is 96.48%, which is found to be better than previous studies. Also, the proposed method can handle faces covered with few new face hiding objects such as hijab, niqab and robber’s ski mask. Furthermore, processing time of the proposed algorithm is significantly improved while it is compared to the existing methods. The detection time varies between 31 and 67 ms which is equivalent to 15–32 frames per second.