role of artificial intelligence in managing customs risk for Algerian customs

Nowadays, the trend toward adopting artificial intelligence (AI) in customs risk management has increased across the globe, yet it may not be efficiently perform the required if the design risk management process is not rich in many features and inputs that are accurate and mathematically applicable. In this paper, we have designed a recommendation system for customs risk management at the level of Algeria’s customs whose current risk management system is still based on the discretion of customs agents, supported by certain internal laws and regulations that guide the decisions of Algerian customs agents. In the design of this recommendation system, we relied on the supervised machine learning where we used five different algorithms. These algorithms have resulted in close accuracy ranging from 97% to 99%. This model reduces the time taken to process different shipments and supports the decision-making process for customs inspectors. However, the current approach of risk management at the Algerian customs level requires greater depth, quality and accuracy at the input level in order to build a highly efficient customs risk management model.

Authors

Institutions

Publication Details

Journal
les cahiers du cread
Published
2024-09-20
DOI
https://doi.org/10.4314/cread.v40i1.10
Citations
1
Primary Topic
Taxation and Compliance Studies
Type
article
Field-Weighted Citation Impact
1.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

role of artificial intelligence in managing customs risk for Algerian customs

Rouzlani Oussama, Bouaziz Nacer, Amroun Wissam
1 citations
les cahiers du cread
Taxation and Compliance Studies
1.13
article

role of artificial intelligence in managing customs risk for Algerian customs

Rouzlani Oussama, Bouaziz Nacer, Amroun Wissam
article en
1 citations

Abstract

Nowadays, the trend toward adopting artificial intelligence (AI) in customs risk management has increased across the globe, yet it may not be efficiently perform the required if the design risk management process is not rich in many features and inputs that are accurate and mathematically applicable. In this paper, we have designed a recommendation system for customs risk management at the level of Algeria’s customs whose current risk management system is still based on the discretion of customs agents, supported by certain internal laws and regulations that guide the decisions of Algerian customs agents. In the design of this recommendation system, we relied on the supervised machine learning where we used five different algorithms. These algorithms have resulted in close accuracy ranging from 97% to 99%. This model reduces the time taken to process different shipments and supports the decision-making process for customs inspectors. However, the current approach of risk management at the Algerian customs level requires greater depth, quality and accuracy at the input level in order to build a highly efficient customs risk management model.

les cahiers du creadVol. 40(1)
Chiang Mai University (TH)
Openalex Percentile: Top 11%
Taxation and Compliance Studies
1.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.