Shipment entity dashboard This allowed analysts to proactively pinpoint patterns of suspicious activity, detect illegal actions and investigate suspected perpetrators of customs fraud and smuggling, rather than relying on random inspections. In addition to the platform’s built-in data fusion, data visualization and analytics capabilities, NEXYTE’s expert team of data scientists quickly developed a tailored, machine learning-based model to automatically score every incoming package according to its likelihood of involvement in customs fraud or tax evasion. The customs organization supplied 2 months' worth of incoming shipment data, which encompassed over 130 million records from 15 different data sources – including 1.6 million merchandise records. Visual link analysis between a suspicious shipment of merchandise and other customs-related entities Manifests Invoices Incidents Data records analyzed Table Records Columns Unique Keys Merchandise 1.6M 38 1,400,000 Carrier 800K 10 665,357 Manifest 7.7M 7 561,289 Containers 1.3M 7 597,162 Invoices 2.9M 20 1,165,700 Dates 3.3M 7 Identifiers 8.2M 9 1,426,918 Contributions 4.3M 9 1,445,513 Batches 23M 31 1,449,775 Permissions 2.3M 12 351,397 Batch level identifiers 54M 10 Batch level rates 12.3M 10 954,457 Batch level contributions 12.4M 10 954,388 Incidents 230K 21 150,276 Selection 3.1M 14