Challenges for Customs Risk Management Today: A Literature Review
Bibliographic record
Abstract
Changes and uncertainty in the customs operating environment and the growth of trade and travel volumes have affected how customs administrations manage and approach their tasks. As a result of technological development, the role of customs in border control has changed dramatically. Thus, the massive volume of goods, the way they are traded worldwide, and the speed of such transactions create additional fiscal, security, financial, and safety risks, affecting the resources available to customs services. The current geopolitical situation has significantly impacted the role of customs services. The topic is relevant to simultaneously assure both the quality of the services provided by the customs and compliance with the requirements set in the framework of limited resources. This study focuses on customs risk management (CRM) issues. It acknowledges that the customs services must continuously improve their operational methods, including promoting a more structured, integrated, and systematic way to manage customs risks. Based on the literature review, we examine the CRM-related challenges and how scholars address them in the scientific literature. This study aims to identify and analyse the contemporary challenges in CRM from its effectiveness point of view. We employ a systematic literature review, searching in most recognised databases and covering the period of 2005–2024. We follow this with a qualitative content analysis and synthesis, summarising and discussing the study results. We identify and discuss relevant key factors contributing to effective CRM. Finally, we conclude with the implications of the findings for CRM practice and policy, as well as with various potential developments in CRM that we suggest for further work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".