Broadpath
QuantaVerse CEO and founder David McLaughlin said that while small steps are having a significant impact, there is much more that can be done. Artificial intelligence can follow the money created by human trafficking operations. That means that instead of identifying only lower-level human traffickers, the “beneficial owners” or kingpins of these operations can be discovered, he said. “AI is better at finding suspicious financial activity than legacy technology alone. Today, banks are required by regulators to report unusual banking activity by filing Suspicious Activity Reports (SARs) to FinCEN. To do this, banks’ anti-money laundering (AML) teams currently rely on antiquated, rules-based Transaction Monitoring Systems (TMS),” McLaughlin explained. “Suspicious transactions are ‘flagged’ by the TMS and handed over to human investigators to determine if a flag should be reported to the authorities. “Unfortunately, 95 percent of the flags produced by the TMS systems are benign and waste vital investigative resources. Even worse, TMS systems are missing crimes that are going through the banking system undiscovered.” McLaughlin said AI automates the investigative digging and then produces reports of its findings. AML investigators can now spend their valuable time/expertise analyzing AI findings and making determinations on what may be human trafficking activity faster than ever. Technology is also playing a prominent role in the private sector, with a community rather than a top-down approach. The revolution of apps has also revolutionized the fight against modern-day slavery, with an added focus on the next step – converting data into something actionable. U.S. researchers developed an AI engine earlier this year, entitled Hotels-50K, which recognizes a hotel from an image of a hotel room -- a critical development for human trafficking investigations. “Images directly link victims to places and can help verify where victims have been trafficked and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms,” the developers wrote. “To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels.” Those images include professionally captured photographs from travel websites and QUANTAVERSE ISSUU FLIP BOOK
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