ฝัง
- เผยแพร่เมื่อ 7 ต.ค. 2019
- This video was recorded in Mountain View on October 3, 2019.
Description:
How do you solve Anti-Money Laundering using Driverless AI? In this
presentation, we will see how to reduce false-positive alerts, which is
a big problem for financial institutions. Using this approach you can
quickly and easily design models that will reduce false-positive alerts significantly while keeping the false-negative number low.
Speaker's Bio:
Ashrith is the security scientist designing anomalous detection algorithms at H2O.ai. He recently graduated from the Center of Education and Research in Information Assurance and Security (CERIAS) at Purdue University with a Ph.D. 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. - วิทยาศาสตร์และเทคโนโลยี
How did u built the rule based system to trigger the alerts. Could you please make another session on that so that we can understand how rule based system should be working?
helpful, Thank you!!
Hi you're using H2Oai's driver less ai for the demo, I can't seem to find this exact tutorial on the cloud environment, can someone please help me find this exact AML use case?
need to answer this question, show use cases,,
is there a dataset used here?
Doing a business of moving a unknown product .
Quite informative, Thanks !
YOU dont define good quality and good for you, need descriptors or suggested standards
32:00
rules, subrules, age, move big sum, someone under 18 big numbers
44:40
It's the _False Negative_ that cost Millions, not _false positives_ ... how stupid