Abstract
In recent years, process mining has been used throughout the world to contribute to the management and development of business processes. Various works have been taken on the steps of process mining and how to analyze business processes. Process mining is of great importance in terms of reducing costs in different sectors, making process improvements and especially shortening times of processes. In this study, the real estate processes offered by the bank are discussed. In real estate transactions, there are processes that differ with many variables. These transactions, these process is long and complicated because of have more checkpoints and operations. Furthermore, end-to-end analysis becomes more difficult because it contains more than one subprocess and integration. Because the processes are not standardized, efficiency studies cannot be disciplined. Records of bank real estate processes obtained over time cannot be used efficiently in process development studies. In the research, how the process mining methodology can be applied in real estate transactions that interact with customers and systems is shown. In addition, it was aimed to show the bottlenecks, long-time processes, work flow and resource statistics in banking process and to reveal the solution suggestions. Process visualizations have been realized with the support of fuzzy model algorithms due to the high number of cases and actions. Process analysis and clustering studies were found to be easier with the help of fuzzy models. The process of obtaining the processes from the data, which is the biggest step of process mining, has been solved by fuzzy models. The results showed that process mining is an important methodology for improving the enterprise processes and increasing their efficiency.
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Yazici, I.E., Engin, O. (2020). Use of Process Mining in Bank Real Estate Transactions and Visualization with Fuzzy Models. In: Kahraman, C., Cebi, S., Cevik Onar, S., Oztaysi, B., Tolga, A., Sari, I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_33
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