Advanced Cargo Analysis (ACA) is a risk assessment solution for government agencies and trading organizations. It assesses the risks of international shipments based on trading documents (customs declarations, cargo manifests bill-of-lading and airwaybill) and additional external and historical data. Based on the risk assessment, ACA identifies cargo that may be dangerous, illegal or otherwise requiring an inspection - before it arrives at the destination port. This improves border security and reduces the opportunity for fraud and smuggling. Depending on the risk, containment or inspection actions are taken. The ACA asset performs this risk assessment in near real time, and produces advice for follow on actions.
ACA handles the workflow towards a rules engine (IBM ODM) and provide an extensive user interface to support the end user to make an informed decision. The asset went into production in June 2020 and can be deployed on-premises as well as on-cloud.
- Automatically assess declaration, bill of lading and airwaybill documents
- Enrichment with historical trader information
- Registration of inspection results to support continuous improvement
- Maintain internal and external watch lists
- Matching of traders against reference data based on Natural Language Processing
- Browser Based User Interface to support manual decisions with build-in task list
- Optional integration with BPM engines for external workflows
- WCO based Data Repository
- Basic rules set with risk profiles that can be extended by client’s experts
- Maintain risk profiles using standard ODM tooling
- Administration for default control instructions, out channels, watch list, trader reconciliation.
Inspection decision and execution are separated, which makes the inspection staff less likely to be exposed to attempts to be bribed.
The rule based systems usually doubles the hit-rate of the inspection staff, allowing either reduction of staff or increase of security.
Traders can be informed in advance, allowing them to better plan their cargo handling.
A roadmap for continuous improvement allowing to extend with machine learning models and usage of additional data sources.
Business Challenge
In modern economies, Port Authorities and Customs Agencies have multiple goals:
Increasing global threats like terrorism and global competition are forcing authorities to find the delicate balance between these goals. It is not practical to control every cargo movement and therefore authorities rely on targeted controls to valige the more suspicious situations
Solution
Advanced Cargo Analytics brings the power of modern analytics to the authorities to perform real time risk assessment based on available documents such as Air Waybills, Sea Bill of Ladings and customs declarations.
Automated risk profiles are used to perform risk assessment and analytics on historical data is used to allow for a continuous improvement.
The Advanced Cargo Analytics can be used as a fully automated selection tool, or as a decision support tool where input documents and found risks are used, aiding the user to regerster a final decision.
The ACA asset is a specialised set of tools to support the specific needs of national targeting centres, customs agencies and port authorities. The assets collect, sort and transform shipment data found logistic documents and customs declarations and then screen this data for the presence of known elements or patterns of behaviour that might indicate a potential threat in the transportation networks. The assets provide warnings to analysts so that they may validate the threat and raise an alert, using the inbuilt workflow and case management contained in the asset.
As such ACA projects need to be implemented with the assistance of subject matter experts in the highly sensitive world of advanced border security. While Projects take between 2 and 5 years from inception to production, the installation and configuration the ACA asset will take around 1 month for the 'out-of-the-box' functionality, assuming that the underlying infrastructure is in place.
Skills Required: IBM ODM