Money Laundering is one of the biggest problems that exists around the world. It affects governments, societies, and legitimate trade. It supports nefarious activities, such as terrorism, human trafficking, illegal trade, and not the least of all, financial stability of a country. The current tools of AML are plagued by high false positives, they involve manual processes which are prone to subjective assessment, and all of this can delay in aiding criminal evasion. In this webinar, we will showcase what we are doing to reduce the false positives, modify the process more objectively and try to assess alerts at line speed. What we build is a mix of ones’ knowledge and artificial intelligence. We will also talk about how you can take advantage of the tools to build basic blocks of a model and then use H2O Driverless AI to build a strong model. Speaker: Ashrith Barthur, H2O.ai
Speaker bio:
Ashrith is the security scientist designing anomalous detection algorithms at H2O. He recently graduated from the Center of Education and Research in Information Assurance and Security (CERIAS) at Purdue University with a PhD in Information security. He is specialized in anomaly detection on networks under the guidance of Dr. William S. Cleveland. He tries to break into anything that has an operating system, sometimes into things that don’t. He has been christened as “The Only Human Network Packet Sniffer” by his advisors. When he is not working he swims and bikes long distances.
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