Human Resource Allocation to the Credit Requirement Process, A Process Mining Approach

In the banking industry, process mining has been used in different case studies with promising results. Process execution efficiency can be improved by correctly allocating human resources, which also increases the revenue and satisfaction of clients in organizations. To gain access to these approaches among the resources, banks are trying to streamline their processes. For this reason, it is essential to take into consideration the prerequisites for an accurate view of the sessions. Nevertheless, few works have tended to process mining as it is connected to the banking industry. This work presents a new approach to solving human resource allocation problems in the credit requirement processes that combines process mining, statistical techniques, and the Theory of Constraint (TOC) method. The statistical techniques are used to build a simulation model. Moreover, to obtain more reliable results from the simulation, in this study, four scenarios are designed to simulate reality. By using it, the bottleneck in the process can be identified. At the end, TOC techniques have been used to tackle the identified bottleneck. This study suggests a combination of resources and activity within fewer iterations compared to the literature and proposes an online process to collect documents which is a second stage of the credit requirement process to overcome the existing bottleneck.