The objective is to present a workload balancing model within the Business Process Management (BpM) framework through a case study of the stochastic, multi-objective, and complex process of analyzing commercial credit applications in a bank. The bank grapples with resource allocation problems-some resources are overloaded while others are underutilized, resulting in suboptimal response times for credit applicants. The applied methodology encompasses queuing theory, production control tools, and operations analysis techniques such as diagrams and characterization of Jackson networks, along with simulation. It was observed that heuristic and linear programming methods are unsuitable for this process due to its complexity. Consequently, statistical analyses were conducted on various input variables using the Minitab 21.2 (R) software. The SIMIO (R) discrete event simulation software modeled the current situation to identify the root causes of the problem. The objective was to maximize installed capacity utilization while avoiding deterioration in metrics like cycle time and Work in Progress (w8). Therefore, a scenario was sought that, while not necessarily optimal, achieved a balance among all indicators, delivering acceptable results in terms of expected performance. This study holds significance for the financial services sector, as it leads to substantial improvements in key performance indicators for a complex process.