Имитационная модель оптимального распределения потока кредитных заявок для межрегионального центра андеррайтинга коммерческого банка
Currently, the main banking activity is associated with the need to make optimal management decisions in the face of considerable variability and uncertainty. Such solutions, as a rule, are based on the processing of very large data sets (Big Data) in real time. Typical examples are the tasks of optimal allocation of loan applications for underwriters, the tasks of maximizing risk-return in the management of loan portfolios (such as RAROC), the task of optimizing non-operating costs (Cost to Income) by redistributing resources by processes.
There is an innovative approach to solving similar problems based on simulation modeling (SM), evolutionary computation and machine learning. This approach has already been successfully applied in leading international and Russian companies. At the same time, simulation models are integrated with corporate enterprise information systems, BPM (Business Process Management System) and ERP systems, information storage (DWH), and are thus used in the actual business processes of the organization as the core of an intelligent decision support system.
This work is devoted to developing the simulation model that is intended for the optimal allocation of credit applications for interregional underwriting center of a commercial bank. The main feature of the model is taking into account many factors affecting on credit applications time processing of underwriters, which are responsible for the estimation a probability of default for credit applications. Such factors are related to the current utilization of underwriters in tasks, accessibility of underwriters for new tasks at the current time, etc. The developed simulation model is implemented in the biggest Russian Bank and used as a part of the BPM-system (Business Process Management).