Document Type : Research Paper
Authors
- Ehsan allah Khoshkhoy Nilash 1
- Mansour Esmaeilpour 2
- Behrooz Bayat 3
- Alireza Isfandyari Moghaddam 4
- Erfan Hassannayebi 5
1 PhD Student of Information Technology Management, Department of Management, Hamedan Branch, Islamic Azad University, Hamedan, Iran
2 Associate Professor, Department of Computer Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran Corresponding Author: esmaeilpour@iauh.ac.ir
3 Assistant Professor, Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran
4 Professor, Department of Knowledge and Information Science, Hamedan Branch, Islamic Azad University, Hamedan, Iran
5 Assistant Professor, Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Abstract
Banks have complex and long-term processes for facilities, including many stages, control points and approvals. Continuous analysis of such processes is increasingly important for continuous improvement and gaining knowledge from them. The main goal of the present research is to provide a comprehensive methodological framework based on process mining and data mining regarding the analysis of fixed capital facility processes. The method used in the present research is derived from the techniques of process mining and data mining based on the event log of the facility system, an active bank in Iran. This method includes nine phases of initiation, preparation, inspection, exploration and analysis, evaluation, multi-dimensional analysis, prediction, review of results and improvement. Among the results of the present research is the extraction of the real process model, identification of bottlenecks, frequent activities in a case and all cases and process variant. In addition to this identification of branches and people with the most important roles and based on data features in reducing the time of payment of facilities, the analysis of the process from dimensions such as the province was one of the other findings. One of the initiatives of the present research was the use of data mining to predict the payment time of facilities. In the comparison of various methods, the decision tree algorithm had the best performance with 72% accuracy. In addition to identifying deviations, based on the creation of event log and its analysis, the improved process of extracting which showed a 67% improvement.
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Main Subjects
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