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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSR</journal-id><journal-title-group><journal-title>Scientific and Social Research</journal-title></journal-title-group><issn>2661-4332</issn><eissn>2981-9946</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/ssr.v6i12.9213</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Artificial Intelligence-Enhanced Risk Management System Architecture for Customs Inspection</title><url>https://artdesignp.com/journal/SSR/6/12/10.26689/ssr.v6i12.9213</url><author>LiMengyao</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>6</volume><issue>12</issue><history><date date-type="pub"><published-time>2024-12-31</published-time></date></history><abstract>The study seeks to boost customs inspection efficiency and ensure compliance with trade data. As traditional methods struggle with the surge in international trade data, this research taps into big data technology to detect anomalies and protect national finances. 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