Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing
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 intellig...
| Main Authors: | , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | https://hdl.handle.net/1721.1/153411 |
| _version_ | 1811089168189620224 |
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| author | Alirezaie, Marjan Hoffman, William Zabihi, Paria Rahnama, Hossein Pentland, Alex |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Alirezaie, Marjan Hoffman, William Zabihi, Paria Rahnama, Hossein Pentland, Alex |
| author_sort | Alirezaie, Marjan |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:14:55Z |
| format | Article |
| id | mit-1721.1/153411 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:14:55Z |
| publishDate | 2024 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1534112024-07-12T16:27:14Z Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing Alirezaie, Marjan Hoffman, William Zabihi, Paria Rahnama, Hossein Pentland, Alex Massachusetts Institute of Technology. Media Laboratory 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. 2024-01-29T16:55:08Z 2024-01-29T16:55:08Z 2024-01-18 2024-01-26T14:11:05Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/153411 Journal of Risk and Financial Management 17 (1): 38 (2024) PUBLISHER_CC http://dx.doi.org/10.3390/jrfm17010038 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Alirezaie, Marjan Hoffman, William Zabihi, Paria Rahnama, Hossein Pentland, Alex Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing |
| title | Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing |
| title_full | Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing |
| title_fullStr | Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing |
| title_full_unstemmed | Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing |
| title_short | Decentralized Data and Artificial Intelligence Orchestration for Transparent and Efficient Small and Medium-Sized Enterprises Trade Financing |
| title_sort | decentralized data and artificial intelligence orchestration for transparent and efficient small and medium sized enterprises trade financing |
| url | https://hdl.handle.net/1721.1/153411 |
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