Intelligent Performance-Aware Adaptation of Control Policies for Optimizing Banking Teller Process Using Machine Learning

In the current banking systems and business processes, the permission granted to employees is controlled and managed by the configured access control methods, in which static role-based models focus on access to information and functions. The deployed configuration is not reviewed/updated systematically and is handled manually by managers. Consequently, banks and companies are looking for systems and applications to automate and optimize their business processes and data management intelligently. In this context, the notion of integrating machine learning (ML) techniques in banking business processes has emerged. In order to build an intelligent and systematic solution, we combine in this paper ML and dynamic authorization techniques to enable performance-based policy evaluation into the banking teller process, where policies adapt to the changes recognized by the ML model. The objective of this work is to focus on the banking teller process that may be generalized to other operational banking processes. In this context, we propose in this paper a new model providing Intelligent Performance-Aware Adaptation of Roles and Policy Control using a support vector machine (SVM). We demonstrate that our model is capable of assessing the deployed control policies and updating them systematically with new roles and authorization levels based on tellers' performance, work history, and system constraints. We evaluated different machine learning models on a real dataset generated from a real-life banking environment. Experimental results explore the relevance and efficiency of our proposed scheme in terms of prediction accuracy, required authorizations, transaction time, and employees' working hours.