Smart Banking

Background: The Internet of things (IoT) is a new technology that expanding rapidly and has led to the emergence of new services in the banking industry. Although IoT is an emerging technology in Iran, it is expected to become a paradigm in the future. IoT applications in banking have helped to provide high quality and fast responses to customers anytime, anywhere. Purpose: This research aims to identify and prioritize IoT applications in the Iranian banking industry based on business continuity indicators. Methods: First, based on the comprehensive literature review and practical experiences, IoT applications in the banking industry are identified. Then, each indicator's weight was calculated using the Grey Decision-making trial and evaluation laboratory (DEMATEL). Finally, IoT-based applications in the Iranian banking industry have prioritized using the multi-attribute utility theory (MAUT). Due to the unstable economic conditions for the financial sector, business continuity indicators are employed in this study. Also, the reason for using MAUT is to consider the utility of banking experts for performing IoT banking services. Findings: According to results, IoT-based services in the banking industry, including Transmit instant reports, smart ATMs, non-contact electronic payments, and electronic checks, are identified for Iranian banks. Then, These appropriate services are prioritized with the capabilities of Iranian banks.

Purpose : The main objective of this research is to present an agile data management system for the intelligent banking industry, by leveraging agile principles, the proposed system aims to support banks in generating data-driven value and smart banking. Method : The research is grounded in the principles of systems thinking, data-centricity, lean thinking, and the agile manifesto. With a practical and developmental purpose, it employs deduction and exploration to draw conclusion. The methodology combines design science and meta-synthesis. Following the design science process, explaining the problem, describing the solution, designing initial solution (using meta-synthesis) validating the solution, and then sharing the results were all steps in the study. Findings : The results consist of codes organized into four-tier structure: design domain, theme, category, axial code, and open code. These are presented as a conceptual model-compromising level zero and level one- which serves as the reference model for an agile data management system for smart banking. Conclusion : An agile data management system, like any other system, it is essential to consider the data management system as having four elements: input (goals), processing (enablers), output (results), and feedback (based on agile and lean thinking). The banking industry has been slow to adopt new technologies, with many banks still reliant on mainframes. This hesitation stems partly from increased caution due to rising cyber-attacks and partly from concerns about regulatory requirements. The proposed model addresses banks' concerns by demonstrating how to integrate agile methods into a data management system for a smart bank.