risk analytic
Watch: Machine Learning for Risk Analytics
With multiple layers of suppliers, artificial intelligence and big data can help companies get clean and useful information, says Jim Hayden, …
Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics
Radanliev, Petar, De Roure, David, Page, Kevin, Van Kleek, Max, Montalvo, Rafael Mantilla, Santos, Omar, Maddox, La Treall, Cannady, Stacy, Burnap, Pete, Anthi, Eirini, Maple, Carsten
This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.
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We are integrating analytics across our monitoring and management software with application experience analytics (AXA) for the best app experience. We are also leveraging advanced analytics and insights across our security portfolio, including payment security with the CA Risk Analytics Network. We are integrating analytics across our monitoring and management software with application experience analytics (AXA) for the best app experience. We are also leveraging advanced analytics and insights across our security portfolio, including payment security with the CA Risk Analytics Network.
3 Chennai startups drive innovation in Soc Gen accelerator - Times of India
CHENNAI: Societe Generale's 10-week accelerator programme Catalyst has eight Indian startups working on various themes and three of them -Uniphore Software Systems, FixNix and Gavs Technologies are from Chennai, working on various risk and analytics tools that may be used by SocGen in future. FixNix, founded by Shanmugavel Sankaran, is trying to develop an early prediction based warning system for employee retention and proactive management of employee expectations. "We have a three-member team working on risk analytics in the accelerator. FixNix is getting aggressively into security and risk analytics for Indian and international banks, including Societe Generale, UBS," said Sankaran. The SaaS based governance, risk and compliance (GRC) startup has funding from ex-CIO of Tesla Jay Vijayan and other angel investors.