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Setting targets for the bank branches and distribution of annual targets to the branches and portfolio managers, quarterly, is a crucial process for decision making and strategic planning in the banking industry. Performance of the bank branches and portfolio managers are also evaluated by the quarterly divided targets to the branches and portfolio managers. In this study, the focus is on performance prediction by using state-of-art machine learning algorithms. A novel automated machine learning approach with combined algorithm selection and hyperparameter optimization is also applied for each of the branches since all the branches might have different customer segmentation and behavior. Moreover, the postconditions are executed to finalize the target calculation and distribution over the performance predictions. The study shows the success of the methodology with a successful application of 98% accuracy in the prediction and majority of branch target calculations. An end-to-end solution found to the seasonality and periodicity problem, which is the biggest problem faced by branches while achieving their goals. Also, the novel approach increases the success of branch targets by 10% in overall. The most significant innovation this study provides to the literature and practitioners is that, unlike classical studies, it solves the seasonality and periodicity problem through multiple time series modeling. The target setting procedure was employed by the largest financial institution in Turkey, Ziraat Bank, to evaluate the operating performance of its branches. The empirical study demonstrates the applicability of the proposed model in the banking sector. The outputs of the study are implemented in real life for all retail branches of Ziraat Bank. In addition, the study awarded the most innovative use of AI/ML, the most innovative project for in-house implementation related to the innovative aspect of the work, by the Global FinTech Innovation Awards 2022.

Performance, Efficiency, and Target Setting for Bank Branches: Time Series with Automated Machine Learning