Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing
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jrfm-17-00038.pdf
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1.11 MB
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Checksum (MD5)
713743895027caa6aca92f1182e32c1a
Author(s)
Alirezaie, Marjan
Hoffman, William
Zabihi, Paria
Rahnama, Hossein
Pentland, Alex
Date Issued
January 18, 2024
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Journal of Risk and Financial Management 17 (1): 38 (2024)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/jrfm17010038
