business intelligence

This paper introduces a framework to enhance the present enterprise resource planning (ERP) systems by integrating big data analytics and state-of-the-art artificial intelligence (AI)-driven cybersecurity mechanisms. Nowadays, due to the diversity and high volume of data, the use of business intelligence and analytics solutions is paramount for ERP systems. This use results in the optimization of enterprise resource planning functions. AI can be used with cybersecurity mechanisms to predict and stop the behavior of a potential threat, thus leading to an optimal cybersecurity model. However, the biggest challenge is the integration of run-time cybersecurity solutions with the enterprise resources in the ERP systems. The proposed solutions incorporate advanced AI-driven cybersecurity techniques for intrusion detection, anomaly detection innovation, and prediction-based mechanisms to mitigate potential threats at the onset. In particular, the paper proposes a set of measures and guidelines for IT stakeholders and business executives on how to integrate technology innovation while maintaining the ERP systems to be modern, relevant, and adaptive in a competitive business environment. We believe that our work is beneficial for both researchers and practitioners to systematically understand the significance of integrating big data analytics with the existing ERP functions and the application of AI in the cybersecurity model for enterprises. Our results emphasize that the implementation of big data and AI-based solutions within the organization will support innovation, safeguard security mechanisms, and lead to a sustainable position in the digital market.

This research study develops for the first time concepts that bring to the forefront an order of magnitude leap in the way financial risk is managed and mitigated by corporations, and in their interactions with their financial services suppliers. At the core of the research work are the development of a specific type of enterprise robotics theories and solution methodologies; end-to-end systems for secure access, navigation, and protection for financial applications using federated identity and attribute management; and the introduction of predictive intelligence using a few dominant principles, modeling, and estimation methods, risk equations, and estimation feedback protocols. In addition to the foundation studies for each of the three major components, we integrate the three into an enterprise financial risk solution and demonstrate through a set of complex activity models and problem derivative-specific applications the nature and degree of financial engineering, financial and data communications, modeling and corporate customer service integration analytics, information and business intelligence, managerial zones, and innovation theories involved.