Improving Financial Technology (FinTech) in Banks Using Process Mining Algorithms | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| دوره 3، شماره 3 - شماره پیاپی 13، زمستان 2023، صفحه 41-50 اصل مقاله (740.31 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2025.33119.1142 | ||
| نویسندگان | ||
| Ali Haghi Nojehdeh1؛ Mansour Esmaeilpour* 2؛ Behrooz Bayat3؛ Alireza Isfandyari Moghaddam3 | ||
| 1Department of Management, Hamedan Branch, Islamic Azad University, Hamedan, Iran. | ||
| 2Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran. | ||
| 3Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran. | ||
| چکیده | ||
| Many analysts believe that the future of the banking industry depends on the generalization and growth of fintechs. The growth and expansion of fintechs in the world indicate their importance in the banking industry. Today, it is important to know more about fintechs and its different parts [1]. Process mining is a new approach based on information technology that seeks to identify and improve the actual process model. Process mining is a chain of events encompassing the beginning and ending stages of a specific activity. Process mining aims to discover, monitor, and improve real-world processes by knowledge extraction from data stored in information systems. Process mining is on the list of new research disciplines, something between data mining and process modeling. In this method, the main ideas are very important, so discovering, monitoring, and enhancing business processes are three important factors in process mining science. This study contributes to the growing body of knowledge in process mining by highlighting the importance of adapting existing algorithms and methodologies to fit the specific needs and conditions of the banking industry, particularly in developing regions. In this research, in the first step, manual and system data related to the studied process were combined to ensure the comprehensiveness of the model, and the level of model details was adjusted based on the opinions of process owners before performing the mining process. After converting the integrated data file to the event log, the process model was implemented using ProM 5.2 and Genetics, Heuristics, Alpha ++, and Alpha algorithms. The results showed that the genetic algorithm has the best performance in issuing credit cards. | ||
| کلیدواژهها | ||
| Fintech؛ Financial Technology؛ Process Mining؛ Banking؛ Mining Technique | ||
| مراجع | ||
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