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

Abstract

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.

Analysis of Security Paradigms for Resource and Infrastructure Management in Global Organizations

“…There is more than one taxonomy, but even so, the ideas used to examine security paradigms for the management of resources and infrastructure in multinational corporations all have straightforward definitions.That is why many people are trying to find a good description of the security infrastructure for multinational corporations. These definitions and paradigms aim to ensure that security experts always speak the same language when discussing various ideas and terms, which will help to detect attacks more accurately and reduce data loss during cyberattacks (Khan et al, 2020;Din et al, 2020;Tay & Mourad, 2020;Yunis et al, 2020;Kumar et al, 2021). The ability to spot security breaches depends on knowing the adversary and its goals, plans, and tactics.…”

Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects

“…Deep leaning techniques were proven efficient in solving multiple automation problems [13][14][15] in a variety of fields including medicine [16][17][18], security [19,20], management [21][22][23][24], and Internet of things [25]. With the proliferation of vision-based deep-learning techniques [26][27][28][29], significant advancements have been made in the generation of potential grasps.…”

Establishing Intent Groups for the Intent-Based Access Control Framework

“…Researchers in [5] considered domain knowledge and purpose of requesting user for accessing the patient record as constraints in current RBAC model to privacy of patient records. B. Tay and A. Mourad in [6] added ML techniques to assess the RBAC access policies enforces in the system, assess the authorization levels based on the user performance and work history to update the policies dynamically. All types of organizations are using the existing RBAC model with some added functionalities depending on their organization's requirements.…”