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The use of a blockchain-based smart import declaration to reduce the need for manual cross-validation by customs authorities

Authors :
Segers, Lennard (author)
Ubacht, J. (author)
Rukanova, B.D. (author)
Tan, Y. (author)
Segers, Lennard (author)
Ubacht, J. (author)
Rukanova, B.D. (author)
Tan, Y. (author)
Publication Year :
2019

Abstract

For determining the correctness of an import declaration lodged by a declarant within international shipping, customs authorities rely on manual cross-validation of the declaration with other trade documents. However, the sheer volume of import declarations lodged annually makes it impossible to manually cross-validate each declaration. Smart contracts have the potential to automatically aggregate relevant information from blockchain-based ledgers for lodging and cross-validation. In this paper we explore how such smart contract could be implemented. We demonstrate that a smart import declaration can be used to automatically aggregate information from different sources (e.g., pro forma invoice data elements stored on the blockchain ledger). However, technical and organizational considerations that are inherent to blockchain technology need to be addressed for the smart import declaration to be acceptable for both customs authorities and declarants. It is therefore suggested that future work focusses on data standards required to enable automatic aggregation of relevant trade information and the generation of the import declaration and governance of the smart import declaration by customs authorities to assure correctness.<br />Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.<br />Information and Communication Technology

Details

Database :
OAIster
Notes :
English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1229974664
Document Type :
Electronic Resource
Full Text :
https://doi.org/10.1145.3325112.3325264