Evaluating the efficiency of organizations and branches within an organization is a challenging issue for managers. Evaluation criteria allow organizations to rank their internal units, identify their position concerning their competitors, and implement strategies for improvement and development purposes. Among the methods that have been applied in the evaluation of bank branches, non-parametric methods have captured the attention of researchers in recent years. One of the most widely used non-parametric methods is the data envelopment analysis (DEA) which leads to promising results. However, the static DEA approaches do not consider the time in the model. Therefore, this paper uses a dynamic DEA (DDEA) method to evaluate the branches of a private Iranian bank over three years (2017-2019). The results are then compared with static DEA. After ranking the branches, they are clustered using the K-means method. Finally, a comprehensive sensitivity analysis approach is introduced to help the managers to decide about changing variables to shift a branch from one cluster to a more efficient one.

But before delving into'behind-the-scenes' of US banking industry meeting ATM, let's turn back time for a second -- on March 27th, 1998, in the New Tech 1998 conference in Denver, Colorado. Here, Neil Postman, a prominent American cultural critic and professor at New York University, gave a keynote lecture. Professor Postman has been a long-time scholar of how new technologies relate to human society, and the book'Amusing Ourselves to Death', a 1985 book that rose to stardom, shows how television technology is destroying public discourse and turning everything into entertainment. I think it has something to do with how we feel about the impact of today's media and how our lives exposed to it are deteriorating. Since this book, Professor Postman has strongly criticized the tendency to respond to all social problems through technical solutions.

Evaluating the banks' performance has always been of interest due to their crucial role in the economic development of each country. Data envelopment analysis (DEA) has been widely used for measuring the performance of bank branches. In the conventional DEA approach, decision making units (DMUs) are regarded as black boxes that transform sets of inputs into sets of outputs without considering the internal interactions taking place within each DMU. Two-stage DEA models are designed to overcome this shortfall. Thus, this paper presented a new two-stage DEA model based on a modification on Enhanced Russell Model. On the other hand, in many situations, such as in a manufacturing system, a production process or a service system, inputs, intermediates and outputs can be given as a fuzzy variable. The main aim of this paper is to build and present a new fuzzy two-stage DEA model for measuring the efficiency of 15 branches of Melli bank in Hamedan province.

This op-ed originally appeared in Real Clear Markets on October 30, 2019. Robots have been coming for and successfully eliminating jobs for a long time: ask the iceman, elevator operator, or travel agent (if you can still find one). But what happens when the robots come for your job, succeed, and your job remains? Sounds strange but consider the conflicting reality of bank tellers and the robot designed to replace them: the Automated Teller Machine (ATM). The first ATM appeared in America in 1969.

Robot lenders are increasingly seen as the future of finance, especially when it comes to loans for smaller companies. Automated algorithmic systems can make credit decisions in an instant, and should be much less hassle for the borrower. But a key question about their widespread adoption is whether small businesses owners used to talking about money matters face-to-face with bankers will trust the digital replacement. Take Royal Bank of Scotland, for example. The company's NatWest unit is rolling out a service for working capital loans for smaller companies, ranging between £25,000 ($31,200) to £300,000.

AI is not really a significant discontinuity, but rather an acceleration of a process that's been going on for thousands of years. AI will lead to more rapid change in the workplace, but whether that leads to joblessness depends more on inequality / wealth distribution than AI. Therefore employment depends on financial governance / redistribution, which can be tricky because AI/ICT facilitate transnational wealth extraction, including trillions of micro barters for information that are not denominated and therefore are hard to tax. Here is a great example of automation not causing joblessness: there are fewer tellers per bank branch due to AI/ATMs, but there are more tellers altogether because there are now more bank branches since they are more profitable. But their jobs are more people oriented, less counting oriented.

The banking industry is very important for an economic cycle of each country and provides some quality of services for us. With the advancement in technology and rapidly increasing of the complexity of the business environment, it has become more competitive than the past so that efficiency analysis in the banking industry attracts much attention in recent years. From many aspects, such analyses at the branch level are more desirable. Evaluating the branch performance with the purpose of eliminating deficiency can be a crucial issue for branch managers to measure branch efficiency. This work not only can lead to a better understanding of bank branch performance but also give further information to enhance managerial decisions to recognize problematic areas. To achieve this purpose, this study presents an integrated approach based on Data Envelopment Analysis (DEA), Clustering algorithms and Polynomial Pattern Classifier for constructing a classifier to identify a class of bank branches. First, the efficiency estimates of individual branches are evaluated by using the DEA approach. Next, when the range and number of classes were identified by experts, the number of clusters is identified by an agglomerative hierarchical clustering algorithm based on some statistical methods. Next, we divide our raw data into k clusters By means of self-organizing map (SOM) neural networks. Finally, all clusters are fed into the reduced multivariate polynomial model to predict the classes of data.

The poor quality of drive-thru ordering may be an old joke (and a staple of comedy movies), but it's also a problem that could benefit from a high-tech overhaul. Machine learning and voice recognition can ease the many pain points of this encounter, contends Denver technology entrepreneur Rob Carpenter, the CEO of Valyant AI. Carpenter's company has developed an artificial intelligence platform that automates fast-food customer service, order-ahead, drive-thru and in-store sales, with technology in development to integrate more directly with point of sale systems. Valyant AI was a recent finalist at a developer program at Visa, and is reportedly in discussions with McDonald's, Walmart and advisors from Yum Brands. Carpenter did not identify his clients, saying the first deployment would come in about four weeks.

A growing number of people are worried that robots -- and other machines with artificial intelligence -- will imminently steal so many jobs that it will lead to a future of pervasive unemployment. But even a cursory reading of history will show that we've been here before. Consider a series of headlines pulled from just one newspaper, the New York Times, as an illustration: In 1928, the Times ran an article titled "March of the Machine Makes Idle Hands." In 1956, it announced "Workers See'Robot Revolution' Depriving Them of Jobs" (for an article about labor unrest in London). In 1980, the newspaper declared "A Robot Is After Your Job."

A robot greets us as we walk in the front door of a local bank branch in downtown Shanghai. Can I help you?" the humanoid receptionist says as we approach. The question is coming from a pretty woman's face encased in a futuristic white helmet. The unmistakably female robot is holding a large touch screen in both hands and seems eager to help. Facial and voice recognition software identify my Mandarin-speaking friend who tells the "receptionist" what she wants before inserting her National Identification Card into a slot to gain entry to the bank. Once inside, a smaller robot trundles over to address us in a piercing child-like voice. We say we are here to open a bank account and it asks us to follow it to one of the automated telling machines at the back of the branch. "Sorry I am very short so I walk slowly.