AI-Enabled Orchestration of Derivatives, Collateral, and Financial Audits in Manufacturing

  • Authors

    • Anumandla Mukesh Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2023PI7K4M

    Published 05-08-2023

  • Cloud Native Financial Orchestration, Derivatives Lifecycle Management, Collateral Management Systems, Multi Counterparty Coordination, Federated AI Audit Engines, Automated Risk Scoring, Anomaly Detection Models, Data Lineage Governance, Financial Data Semantics, Cloud Based Data Lakes, Event Driven Architecture, AI Assisted Compliance Monitoring, Pattern Matching Risk Analytics, Knowledge Assisted Decision Support, Public Cloud Infrastructure, Web Service Interoperability, Manufacturing Enterprise Finance, Multisided Market Systems, Centre Periphery Orchestration, Enterprise Scale Financial Automation

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    Mukesh, A. (2023). AI-Enabled Orchestration of Derivatives, Collateral, and Financial Audits in Manufacturing. International Journal of Commerce, Finance and Digital Economy, 6(1), 01-14. https://doi.org/10.67228/3071642X/IJCFDE-2023PI7K4M

  • Abstract

    Global manufacturing enterprises often negotiate and manage numerous derivative and collateral agreements with several counterparties. However, there has been limited consideration of AI-supported systems for orchestrating these transactions across parties in public cloud environments. The research objective is to demonstrate constitutive elements of an enterprise-wide cloud-native orchestration system for derivatives and collateral across multiple counterparties, supported by a federated network of AI-assisted financial audit engines, within a specified research framework. Derivatives- and collateral-related data are identified and classified, enabling the definition of the data lineage, schema, and standards required by the orchestration system. Expression of data semantics and content for counterparties is addressed with reference to web services and cloud-based data lakes. In parallel, an automated risk-scoring AI engine capable of detecting patterns and anomalous derivatives structures configures legitimate regions of the derivatives parameters space, which are adopted as input to the final engine performing pattern-matching and knowledge-assisted risk-scoring and/or decision-support assessments for bank partnering and hedge-derivative counterparties. Effective cloud-native centre-periphery interactive orchestration is achieved among cloud-based AI assistants managing derivatives–collateral business across multiple manufacturing enterprises and their financial-contracting supply-chain counterparties. The proposed design is a first step toward addressing multisided market systems in the manufacturing enterprise sector.

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