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

Abstract

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.

Being a game changer with happy workers: The Ziraat Bank example

“…Annex: Definitions used in the study; Performance score; consists of the points that the employees receive from groups, such as sales of banking products, acquiring new customers, and maintaining existing customers, which are given as targets every three months [ 61 ].…”

Multi-Objective Location Problem for Bank Branches

“…In [13] efficiency performance of bank branches together and identification of influential is studied using a multi-step procedure that uses clustering and data envelopment analysis. In [14] the performance of the bank branches and portfolio managers is predicted using well-known machine learning algorithms.…”

Setting Goals and Objectives of Enterprises

“…The data set to be used is a Retail Data Set [9], which contains information about a wholesaler's sales over a period of 3 years. We have followed the cross industry standard processing for data mining (CRISP-DM) methodology and as a part of the methodology, we have started with analysis, discovery and the organization of the data set [9].…”

“…In this step, parallel to the CRISP-DM methodology, we have studied to uncover valuable and actionable information from data and provide strategic decision support [9]. By utilizing statistical methods, data mining techniques, and visualization tools, data analysis transforms data into meaningful insights [9].…”