ARTIFICIAL INTELLIGENCE FOR THE DETECTION OF TRADE-BASED MONEY LAUNDERING, SMUGGLING, BRAND AND PRODUCT PIRACY

Demo

ATTENTION! (ArTificial inTelligENce for the deTectIon of trade-based mOney lauNdering!) is an international 3-year project that started in July 2022, enabled by ITEA and funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) and Enterprise Singapore.

The Project | Web Crawler | Data Model & Red Flags | Company Networks | Trade Data | Expected Key Results | Contact
These demonstrations show preliminary, exemplary and partially anonymised results. This website is not optimised for mobile browsers.

The Project
1 / 3
ATTENTION! - ArTificial inTelligENce for the deTectIon of trade-based mOney lauNdering!
2 / 3
Project Components
3 / 3
Project Partners
❮ ❯

Web Crawler
Example output of the web crawler searching for a set of websites about companies offering trailers

Goods are traded on the web, and thus, we find relevant company and product information on the web. For example, web data can help to identify vendors of potentially illicit products by their online offers and gathers and enriches data about the online presence of said vendors.

The goal of ATTENTION!'s Web Crawler is to identify vendors of potentially illicit products using their online offers as well as to gather web data on these actors since several red flags and patterns of illicit trades require information not present in company databases.

This includes information about actual products offered by the vendor (brands, product descriptions, prices, images), company metadata such as the (claimed) company location, and behavioral / relational data such as where potentially illicit vendors advertise or which websites are linking to theirs.

Data Model & Red Flags
1 / 2
Data Model as an RDF Ontology
2 / 2

Example Query 1

The company is located in a country that is known as a tax haven.

PREFIX att: <https://attention-project.eu/rdf>
PREFIX schema: <https://schema.org/>

SELECT DISTINCT ?organization
WHERE {
  ?organization a schema:Organization ;
    schema:address ?address .
  ?address a schema:PostalAddress ;
    schema:addressCountry ?country .
  ?country a schema:Country ;
    att:isTaxHaven true .
}

Example Query 2

The weight of traded good mismatches the average for similar transactions.

PREFIX att: <https://attention-project.eu/rdf/>
PREFIX schema: <https://schema.org/>

SELECT ?company ?product ?weight
WHERE {
  ?tradeaction a schema:TradeAction ;
    schema:seller ?company ;
    att:tradedProduct ?product .
  ?company a schema:Organization .
  ?product schema:weight ?weight ;
    att:productCategory ?category .
  ?category att:hsCode ?hsCode ;
    att:avgWeight ?averageWeight .
  FILTER (?weight != ?averageWeight)
}
Example Red Flags as SPARQL Queries
❮ ❯

Company Networks
Example company network where certified retailers are marked in green and known suspicious companys are marked in red.

To understand patterns behind complex, large scale illicit trade and its perpetrators, it is relevant to understand company networks and to investigate entities that could be behind complex trade-based money laundering structures.

Company networks describes companies, their attributes (e.g., revenue and address) and relations (e.g., subsidiaries).

Trade Data
Example vessel movement close to a port

Trade data is about import/export transactions (example of a trade transaction: a good of category "Furniture" that is delivered from Hamburg to Singapore on May 6, 2023), and it is relevant to iunderstand the flow of goods between companies.

By connecting trade data with vessel positions, we aim at the detection of spatio-temporal patterns and anomalies in global trade.

Expected Key Results
  • Analysis of the largest trade database of imports and exports together with extensive web content and metadata
  • Machine Learning models to understand and detect patterns of illicit trade activity
  • MVP that will empower companies and authorities to better detect illicit trade

Contact