Hybrid AI and Digital Twin Architecture for Enterprise Commerce and Financial Transformation

  • Authors

    • Alan Bundy Professor of Artificial Intelligence, University of Edinburgh, United Kingdom Author
    • Karen Spärck Jones Professor, University of Cambridge, United Kingdom Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2021PI7V1G

    Published 06-05-2021

  • Hybrid Artificial Intelligence, Digital Twin, Enterprise Commerce, Financial Transformation, Machine Learning, Digital Enterprise, Intelligent Automation, Predictive Analytics, Business Intelligence, Explainable AI

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    A. Bundy and K. S. Jones, “Hybrid AI and Digital Twin Architecture for Enterprise Commerce and Financial Transformation”, IJDEIC, vol. 4, no. 1, pp. 01–18, Jun. 2021, doi: 10.67228/30715717/IJDEIC-2021PI7V1G.

  • Abstract

    Enterprise commerce and financial ecosystems are being transformed by Artificial Intelligence (AI), Cloud Computing, Big Data, Blockchain, the Internet of Things (IoT), and intelligent automation. However, conventional enterprise information systems primarily support operational automation and often lack real-time predictive intelligence, adaptive decision-making, and integrated visibility across business processes. These limitations lead to data silos, inaccurate forecasting, operational inefficiencies, financial risks, and reduced organizational agility. This paper proposes a Hybrid AI and Digital Twin Architecture for Enterprise Commerce and Financial Transformation that integrates intelligent data acquisition, digital twin modeling, hybrid AI analytics, financial intelligence, enterprise optimization, and continuous learning into a unified computational framework. The architecture combines machine learning, deep learning, reinforcement learning, knowledge graphs, explainable AI, and optimization techniques with dynamic digital twins that continuously synchronize with data from ERP, CRM, IoT devices, cloud platforms, and financial systems. The proposed framework enables predictive analytics, business process simulation, anomaly detection, financial forecasting, automated risk assessment, and explainable decision support. Organizations can evaluate multiple operational scenarios within a virtual environment before implementation, thereby reducing uncertainty, improving resilience, and enhancing strategic planning. Mathematical models are also introduced to evaluate enterprise operational efficiency, digital twin synchronization, predictive intelligence, and financial transformation performance. The proposed architecture is expected to improve forecasting accuracy, optimize supply chain operations, strengthen fraud detection, reduce operational costs, enhance customer experience, support regulatory compliance, and promote sustainable enterprise growth. By integrating Hybrid AI with Digital Twin technology, the framework provides a scalable and intelligent foundation for autonomous commerce, resilient financial management, and data-driven enterprise transformation across diverse industrial sectors.

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