Improving Financial Technology (FinTech) in Banks Using Process Mining Algorithms

Document Type : Research Paper

Authors

1 Department of Management, Hamedan Branch, Islamic Azad University, Hamedan, Iran.

2 Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran.

3 Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran.

Abstract

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.

Keywords

Main Subjects

References

  • Anshari, M., Almunawar, M. N. and Masri, M. (2019). “An Overview of Financial Technology in Indonesia”. In Financial Technology and Disruptive Innovation in ASEAN. IGI Global, PP. 216-224.
  • Gomber, P., Koch, J. A. and Siering, M. (2017). “Digital Finance and FinTech: current research and future research directions”. Journal of Business Economics, 87(5), pp. 537-580.
  • Liu, J., Li, X. & Wang, S. (2020). What have we learned from 10 years of Fintech research? A scientometric analysis. Technological Forecasting and Social Change, Elsevier, 155(C): 1-13.
  • Haghshenas, Farzaneh. (2009). Credit Card Survey in Iran, 3rd World Conference on Electronic Banking, Tehran.
  • Elhami, Erfan and Shams Aliei, Fereydoun and Farahani. (2018). Investigating the Challenges in Business Process Predictive Monitoring Methods, 2nd National Conference on Organizational Architecture Advances, Shiraz.
  • Ereydoun Shams Ali, Leila Heidari, Mahmoud Neshati. (2020). An Approach to Improve Organizational Processes Using Process Mining Techniques Case Study: Quality Control Process of Iranian National Smart Card Applications, Journal of Computer Science, 3 (18), 3.
  • Mostafaei Dolatabad, Khadijeh, and Azar, Adel and Moqbel Ba'arz, Abbas and Parvizian, Kourosh (2019). evaluation of process mining in discovering semi-automatic processes of the banking industry (a case study of the bank guarantee issuance process).
  • Van der Aalst, W. M. (2022). Process mining: a 360-degree overview. In Process Mining Handbook (pp. 3-34). Springer, Cham.
  • Brock, J., Brennig, K., Löhr, B., Bartelheimer, C., von Enzberg, S., & Dumitrescu, R. (2024). Improving Process Mining Maturity–From Intentions to Actions. Business & Information Systems Engineering, 66(5), 585-605.
  • Bocciarelli, P., & D’Ambrogio, A. (2024). Simulation-based predictive process mining with ebpmn: methods, challenges, and opportunities. In 2024 Annual Modeling and Simulation Conference (ANNSIM) (pp. 1-14). IEEE.
  • Jochen De Weerdt, J., A. Schupp, A.Vanderloock, and B. Baesens. (2013). Process Mining for the multi-faceted analysis of business processes.
  • Maldonado, A., Frey, C. M., Tavares, G. M., Rehwald, N., & Seidl, T. (2024). GEDI: Generating Event Data with Intentional Features for Benchmarking Process Mining. In International Conference on Business Process Management(pp. 221-237). Cham: Springer Nature Switzerland.
  • Jans M., J.M. van der Werf, N. Lybaert and K. Vanhoof. (2011). A business process mining application for internal transaction fraud mitigation, Expert Systems with Applications 38 (10): 13351–13359.
Modeling and Simulation in Electrical and Electronics Engineering
Volume 3, Issue 3 - Serial Number 13
November 2023
Pages 41-50

Files

History

  • Receive Date: 28 January 2024
  • Revise Date: 23 November 2024
  • Accept Date: 29 January 2025

Share

How to cite

Statistics