Data analysis for effective border management

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 modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making." Collecting and analysing data is becoming extremely important in Customs modernisation process. Multifarious data is available with Customs which includes data of Customs clearances, data obtained from other departments and data available from open sources.
Since the international trade and number of travellers have exponentially increased, therefore big data is being produced by these trade transactions and itineraries. About 90% of the world's cargo is transported in maritime containers, but only 2% is physically inspected by Customs authorities, opening the possibility for illicit activities. Today, it is widely believed that the only viable way to control containerised cargo is through information-based risk analysis. In this way, it becomes possible to target high-risk shipments and proceed with physical checks, only where needed.
With the sheer volume of goods being imported and exported, Customs are dealing with a mass amount of Customs declarations and are unable to review every declaration for clearance, therefore, they rely on the data that is available with them. With the effect of the e-Customs decision in moving towards a paperless environment, most countries have moved towards the electronic use of providing their import and export declarations. Customs are relying heavily on import / export information to identify trends, track shipments, detect errors, and identify non-compliance of the movement of goods in real time. This information all begins with data. Having access to import / export data provides insight into the physical flow of goods being shipped from source to destination which in turn allows Customs to perform various in-depth analyses.
Customs authorities face a number of challenges in supervising the cross border movement of goods to ensure all regulatory requirements are met while also ensuring that clearance times are kept to a minimum to prevent the backlog of goods entering into the country.
By bringing all the data together from the entire logistic chain, Customs can obtain accurate pictures enabling them to identify trends understanding who does what along the chain. Data from trusted traders such as Authorised Economic Operators (AEO) can be treated as reliable data, allowing goods to be cleared immediately without inspection. Identifying trends can warn Customs authorities of suspicious activities which can lead to the detection of fraud, smuggled goods and counterfeit products. Doing this manually is time consuming and can result in errors. Advanced 'data analytics' such as 'predictive analytics' can enable Customs to risk rank import and export transactions and create risk scores in real time, eliminating trustworthy traders while predicating and preventing fraudulent shipments.
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 analyses and information sharing for more effect border management and revenue generation. For instance WCO Data Model supports data analysis by improving data collection and enabling the sharing of data between government agencies. The WCO Customs Enforcements Network (CEN) is a global Customs seizure database for sharing of information regarding prominent seizures made by the Customs administrations of the member countries. The sharing of information with Customs administrations of other countries helps to know about the new patterns being used for smuggling of goods / narcotics and take preventive measures for detection of such cases for safer borders.
Data analysis is used by Customs administrations for risk assessment and clearance of passengers arriving into and departing from a country. Passenger Name Record (PNR) data records the passenger information of persons taking or proposing to take international passenger air service flights. Access to Passenger Name Record data forms an integral component of intelligence led, risk based approach to border protection. Analysis of pre-arrival or departure PNR data and other relevant information by Customs administrations and partner agencies plays a critical role in the identification of possible persons of interest in the context of combating terrorism, drug trafficking, identity fraud, people smuggling and other serious transnational crimes.
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 harmonisation 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 analyse 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.
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 modelling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making." Collecting and analysing data is becoming extremely important in Customs modernisation process. Multifarious data is available with Customs which includes data of Customs clearances, data obtained from other departments and data available from open sources.
Since the international trade and number of travellers have exponentially increased, therefore big data is being produced by these trade transactions and itineraries. About 90% of the world's cargo is transported in maritime containers, but only 2% is physically inspected by Customs authorities, opening the possibility for illicit activities. Today, it is widely believed that the only viable way to control containerised cargo is through information-based risk analysis. In this way, it becomes possible to target high-risk shipments and proceed with physical checks, only where needed.
With the sheer volume of goods being imported and exported, Customs are dealing with a mass amount of Customs declarations and are unable to review every declaration for clearance, therefore, they rely on the data that is available with them. With the effect of the e-Customs decision in moving towards a paperless environment, most countries have moved towards the electronic use of providing their import and export declarations. Customs are relying heavily on import / export information to identify trends, track shipments, detect errors, and identify non-compliance of the movement of goods in real time. This information all begins with data. Having access to import / export data provides insight into the physical flow of goods being shipped from source to destination which in turn allows Customs to perform various in-depth analyses.
Customs authorities face a number of challenges in supervising the cross border movement of goods to ensure all regulatory requirements are met while also ensuring that clearance times are kept to a minimum to prevent the backlog of goods entering into the country.
By bringing all the data together from the entire logistic chain, Customs can obtain accurate pictures enabling them to identify trends understanding who does what along the chain. Data from trusted traders such as Authorised Economic Operators (AEO) can be treated as reliable data, allowing goods to be cleared immediately without inspection. Identifying trends can warn Customs authorities of suspicious activities which can lead to the detection of fraud, smuggled goods and counterfeit products. Doing this manually is time consuming and can result in errors. Advanced 'data analytics' such as 'predictive analytics' can enable Customs to risk rank import and export transactions and create risk scores in real time, eliminating trustworthy traders while predicating and preventing fraudulent shipments.
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 analyses and information sharing for more effect border management and revenue generation. For instance WCO Data Model supports data analysis by improving data collection and enabling the sharing of data between government agencies. The WCO Customs Enforcements Network (CEN) is a global Customs seizure database for sharing of information regarding prominent seizures made by the Customs administrations of the member countries. The sharing of information with Customs administrations of other countries helps to know about the new patterns being used for smuggling of goods / narcotics and take preventive measures for detection of such cases for safer borders.
Data analysis is used by Customs administrations for risk assessment and clearance of passengers arriving into and departing from a country. Passenger Name Record (PNR) data records the passenger information of persons taking or proposing to take international passenger air service flights. Access to Passenger Name Record data forms an integral component of intelligence led, risk based approach to border protection. Analysis of pre-arrival or departure PNR data and other relevant information by Customs administrations and partner agencies plays a critical role in the identification of possible persons of interest in the context of combating terrorism, drug trafficking, identity fraud, people smuggling and other serious transnational crimes.
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 harmonisation 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 analyse 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.
(The writer is Deputy Director, Directorate of Reforms & Automation (Customs), Custom House, Karachi)