In 2017, computer scientist Rebecca Portnoff and her colleagues explained how cryptocurrency can help us follow the breadcrumbs of sex traffickers. The team developed a machine learning classifier that takes advantage of stylometry — the study of writing style via quantitative processing — to first distinguish between independent sex service providers and potential sex traffickers on online sex advertisements. From there, computational linking techniques make use of leakages in the cryptocurrency ‘mempool’ (a space that holds pending cryptocurrency transactions) to link back to those potential traffickers. This promising technology may, however, see some limitations beyond its control, such as the inherent limits of stylometry (or rather, forensic linguistics in general).
Red flag indicators related to transactions can involve payments that are made in small amounts, or in repeated quantities that fall under a reporting threshold. Alarm bells can also be raised if funds are sent to a newly created or previously inactive account.
Transaction patterns can also rouse suspicion — especially if the deposits made are inconsistent with a customer’s profile.
Other indicators can concern senders and recipients, irregularities when it comes to the source of funds or wealth, and suspicious circumstances related to geography — such as if a customer’s funds originate from, or are sent to, an exchange “that is not registered in the jurisdiction where either the customer or the exchange is located