Article: Rizwan Mahmood Deputy Director, Directorate of Reforms & Automation (Customs), Custom House, Karachi |

Article: Rizwan Mahmood Deputy Director, Directorate of Reforms & Automation (Customs), Custom House, Karachi

[caption id="attachment_22292" align="alignleft" width="150"] Rizwan Mahmood Deputy Director, Directorate of Reforms & Automation (Customs), Custom House, Karachi[/caption] Effective border management World Customs Organization (WCO) has dedicated 2017 to promote the use of data analysis under the slogan “Data Analysis for Effective Border Management.” Data Analysis is defined as a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. Collecting and analyzing data is becoming extremely important in Customs modernization process. Multifarious data is available with Customs which includes data of Customs clearances, data obtained from other departments and data available from open sources. Keeping in view the technological advancements, Customs authorities are increasingly using the substantial amount of Customs data at their disposal in order to make their controls more effective and reveal gaps. This has been further strengthened by the WCO’s initiative using various tools, studies and guidance. WCO has developed certain tools for data analysis and information sharing for more effect border management and revenue generation. Customs authorities consider the transportation route as an important factor for the profiling and targeting of high-risk cargo containers. In most of the cases, authorities have incomplete information. On the other hand, ocean carriers collect, store and own Container Status Messages (CSM) which describes the status and movement of the containers. Semantic information can be extracted from the CSM data in the form of Container-Trip Information (CTI) and Vessel-Stop Information (VSI). These new information elements can be used to build route-based risk indicators for the automated analysis of the routes.  Through deep-web data mining, semantic data integration, sequence data mining, container itinerary analysis, semantic trajectory clustering and statistical analysis, the ConTraffic system of European Union not only collects and creates a historical data warehouse of container itineraries, but also generates meaningful risk-related information for Customs officers. However, there are certain potential impediments to an optimal use of data such as the lack of qualitative data, data that has been integrated or merged, lack of harmonization of data across border agencies lack of skilled resources, IT challenges and cultural challenges. In addition it is vital that appropriate privacy and confidentially law be respected. Despite these limitations, the collection of data is worthwhile if it is used analytically, efficiently and effectively for making informed decisions regarding challenges of compliance and facilitation being faced by the Customs administrations today. Data analysis can be helpful in achieving core Customs objectives of revenue collection, trade facilitation, border security and collection of trade statistics. However, to achieve these objectives Customs administration need to have the appropriate automation policies, latest technology and expert hands to analyze the data for meaningful applications. Given the sophisticated, evolving threats Customs agencies deal with every day, it’s especially critical that they leverage big data to make informed decisions. Therefore, data analysis can be a successful tool for Customs to improve risk managements for detection of illicit consignments, the suspicious movement of people involved in various smuggling activities and drug trafficking for ensuring effective border management.
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