Handbook of Customs Risk Management with Data and AI

Customs administrations already have the data. The real challenge is turning it into decisions that officers can act on, traders can understand and contest, and auditors can reconstruct. Many analytics books stop at model accuracy. In customs, that is where the work begins. This handbook covers the whole path from data to decision. It brings together customs risk management, statistics, machine learning, explainability, governance and deployment around one central principle: a score is not a decision. Across thirty chapters, the reader moves from data quality and mirror-trade analysis to risk scoring, calibration, drift monitoring and production deployment under the EU AI Act, with each method tied to targeting, selectivity and control. Key Features Built for customs, not adapted to it. Methods address practical questions: whom to inspect, what evidence supports intervention, and how targeting affects legitimate trade. Reproducible, and verified as such. More than forty Python examples, companion notebooks and a synthetic data generator support reproducibility. Before publication, thirty-two output-bearing listings were regenerated from the shipped code and checked against the manuscript. Clear about uncertainty. Results are reported with confidence intervals, including a worked case in which a model fails its pre-specified deployment criterion because the evidence is not strong enough. Confronts difficult operational problems. The book addresses rare events, selection bias, calibration, explainability, network analytics and HS classification, as well as how traders respond when targeting changes. Keeps accountability in the loop. Human oversight, traceability, proportionality and auditability are treated as design constraints, not compliance afterthoughts. Written for customs leaders, risk managers, analysts, data scientists, policymakers and students, this handbook is both an implementation guide and a strategic reference. Its argument is practical: value comes not from more sophisticated models, but from decisions that can be measured, defended and, when the evidence changes, overturned. This book is supported by additional online materials which include a synthetic customs dataset, a data generator, Python notebooks, figure-reproduction scripts, supporting templates, environment requirements, and reader instructions.