Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing

Marjan Alirezaie (), William Hoffman, Paria Zabihi, Hossein Rahnama and Alex Pentland
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Marjan Alirezaie: Flybits Labs, TMU Creative AI Hub, Toronto, ON M5B 2K3, Canada
William Hoffman: Flybits Labs, TMU Creative AI Hub, Toronto, ON M5B 2K3, Canada
Paria Zabihi: Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Hossein Rahnama: MIT Media Lab, Cambridge, MA 02139, USA
Alex Pentland: MIT Media Lab, Cambridge, MA 02139, USA

JRFM, 2024, vol. 17, issue 1, 1-16

Abstract: The complexities arising from disparate data sources, conflicting contracts, residency requirements, and the demand for multiple AI models in trade finance supply chains have hindered small and medium-sized enterprises (SMEs) with limited resources from harnessing the benefits of artificial intelligence (AI) capabilities, which could otherwise enhance their business efficiency and predictability. This paper introduces a decentralized AI orchestration framework that prioritizes transparency and explainability, offering valuable insights to funders, such as banks, and aiding them in overcoming the challenges associated with assessing SMEs’ financial credibility. By utilizing an orchestration technique involving symbolic reasoners, language models, and data-driven predictive tools, the framework empowers funders to make more informed decisions regarding cash flow prediction, finance rate optimization, and ecosystem risk assessment, ultimately facilitating improved access to pre-shipment trade finance for SMEs and enhancing overall supply chain operations.

Keywords: orchestrated AI; decentralized data; SME trade financing (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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