Systems thinking

Banks as the largest financial intermediaries play a vital role in collecting savings and directing them toward manufacturing activities. Hence, assets and liabilities management is crucial for them and their depositors. In this study, through system dynamics approach, a dynamic model was proposed to manage assets and liabili-ties optimally. After building the model and conducting validation and sensitivity analysis, three scenarios were devised and were simulated and analyzed via the proposed model. The results revealed that taking effective decisions and actions to collect more deposits, especially time deposits as well as increasing the quality of credit assets (if allocated optimally) can be effective in improving the performance of banks. Therefore, using systems thinking and system dynamics can aid banks in managing their assets and liabilities optimally. mary of the significant items in the paper, including the results and conclusions.

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.