Minh-Hoang Tran,1 Ngoc Quy Nguyen,2 Hong Tham Pham1,3 1Department of Pharmacy, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam; 2Institute of Environmental Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 3Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Correspondence: Hong Tham Pham, Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam, Tel 84 919 559 085, Email [email protected] Abstract: Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague.

One thing that makes ISIS so hard to fight is that the terrorist network is diffuse and scattered, with small cells of operatives all over the world. Not only does this make it hard for law enforcement to predict where the group might strike next; it makes it incredibly complicated to track activity on the network--activity like banking transactions. Small sums of money flow from foreign fighter to foreign fighter, yet banks struggle to identify it within their systems. Banks have long used anti-money laundering systems to flag suspicious activity, and in the aftermath of September 11th, they have turned to those same legacy tools to catch terror-related transactions, too. But these legacy tools are not up to the job.

Wayne, PA (June 7, 2017) –QuantaVerse, innovator of data science and artificial intelligence (AI) solutions purpose-built for identifying financial crimes, today announced a complimentary analysis that financial institutions can use to test the effectiveness of their AML programs. The CCO (Chief Compliance Officer) Checkup is a free service whereby QuantaVerse's AI solution will analyze transaction data to detect "false negatives" or anomalous behaviors that may have been missed by an institution's existing transaction monitoring system (TMS). These risks take the form of fines, penalties and forfeitures for violations of BSA and other regulations. Despite increased investments in AML systems, this risk is increasing year over year. In 2016 alone, $42 billion in fines for AML non-compliance were paid to federal regulators by the largest North American and European financial institutions.