Availability of large data sets and increased computing performance have contributed to many improvements in productivity and decision-making. Simulation can exploit these by incorporating data mining capabilities, such as machine learning, in the modeling and analysis process. This paper demonstrates the integration of discrete event simulation with a deep learning resource, known as TensorFlow, to enable intelligent decision making in the form of smart processes. A bank credit approval process is modeled using these smart processes to evaluate customer credit worthiness based on 20 reported features. Comparison of three models is made where credit worthiness is (1) known, (2) randomly assigned, or (3) evaluated based on customer features. Additionally, the experiment compares results under conditions where the process is perturbed by an unexpected surge in customer arrivals. The presented models and results demonstrate the feasibility of enabling smart processes in discrete event simulation software and the improved decision-making fidelity.