ATTENTION!
ArTificial inTelligENce for the deTectIon of trade-based mOney lauNdering!
Project description
Illicit trade is a problem of massive proportions, yet the detection rate is close to zero worldwide. Currently, there are very few methods to detect complex, large-scale cases of money laundering in trade and trafficking in counterfeit products. The necessary investigations are time-consuming and costly. Worldwide, there is a lack of a scientifically sound understanding of how illegal transactions can be detected and what patterns they follow.
The ATTENTION! project will systematically collect, model and analyse multiple global data sources such as trade data for imports and exports, company and web data. Based on global trade activities and their contextual information, artificial intelligence (AI) models will be developed and applied to detect and uncover illicit trade activities and their patterns in global, heterogeneous data. Among the challenges to be overcome are the lack of known cases, the complexity of the patterns and the need to explain detected suspicious cases. To this end, ATTENTION! relies on the interplay of supervised and unsupervised learning as well as the creation of a knowledge graph.
The project will result in AI models, services and applications that enable end-users (e.g. manufacturers, buyers, customs authorities, law enforcement agencies and banks) to perform checks on commercial transactions, product offers and trader profiles and to detect patterns of illegal transactions in them in order to identify whether goods bought, sold or financed could be at risk. The project results will be validated in selected application scenarios together with business users and exploited by the interdisciplinary ATTENTION! consortium.
Consortium
Germany: Leibniz Universität Hannover (Forschungszentrum L3S), HASE & IGEL GmbH, SCHAEFFLER AG, Rheinische Friedrich-Wilhelms-Universität Bonn
Singapore: RisikoTek
Duration
July 2022 – June 2025
Budget (Germany)
Total costs: € 2.4 million
Funding volume: € 2.0 million
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