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Fighting Illicit Trade With Artificial Intelligence

AI has opened doors to many transformation opportunities and increasingly minimised many risks  — personal and economic — that are alarming today. And illicit trade is one of those pains AI can offer a promising solution against.
Illicit trade is a serious threat and problem that affects governments and societies on every level. While governments lose financial funds in tax revenues, thriving businesses are losing potential customers, and customers are getting tricked into purchasing counterfeit, low-quality products. Transnational organized crime generates revenue of $2.2 trillion through transnational criminal organizations, complicit corrupt facilitators, and other threat areas.
The list of criminal activities is long and involves such horrific crimes as trafficking of narcotics, opioids, arms, humans, fake medicines and other counterfeit and pirated goods; illegal tobacco and alcohol; illegally-harvested timber, wildlife, and fish; pillaged oil, diamonds, gold, and other natural resources and precious minerals; stolen antiquities; and other contraband or valuable items sold across streets, social media, online marketplaces, and the dark web.
In short, illicit trade is a contributing cause to large-scale insecurity and instability across markets.
AI-driven technologies
Here is where revolutionary AI-driven technologies come in, with their capability to fight illicit trade across markets. AI technologies in this specific application promise to help build safer and more secure communities in the future. There are a few ways that AI can support the ongoing fight against illicit trade on a global scale in a tangible way.
Transnational organized crime generates revenue of $2.2 trillion
For financial transactions at risk of fraud and money laundering, tracking has become an increasing headache if done manually. As a solution to this labour-intensive process, AI technology can be trained to follow all the compliance rules and process a large number of documents — often…

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