Graph-based solutions have been in the market for over a decade with deployments in financial services, healthcare, retail, and manufacturing. The graph technology of the past limited them to simple queries (1 or 2 hops), modest data sizes, or slow response times, which limited their value. A new generation of fast, scalable graph databases, led by TigerGraph, is opening up a new world of business insight and performance.
Join us, as we explore some new exciting use cases powered by native parallel graph database with storage and computation capability for each node:
• A large financial services payment provider is using graph-based pattern detection (7 to 11 hop queries) to detect more fraud and money laundering in real time, handling peak volume of 256,000 transactions per second.
• IceKredit, an innovative FinTech is transforming the near-prime and sub-prime credit market in United States, China and South Asian countries with customer 360 analytics for credit approval and ongoing monitoring.
• A biotech and pharmaceutical giant is building a prescriber and patient 360 graph and using multi-hop exploratory and analytic queries to understand the most efficient ways of launching a new drug for maximum return.
• Wish.com is delivering real-time personalized recommendations to increase eCommerce revenue.
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Dr. Victor Lee is Senior Product Manager at TigerGraph, bringing together a strong academic background, decades of experience in the technology sector, and a strong commitment to quality and serving customer needs. His first stint in Silicon Valley was as an IC circuit designer and technology transfer manager, before returning to school for his computer science PhD, focusing on graph data mining. He received his BS in Electrical Engineering and Computer Science from UC Berkeley, MS in Electrical Engineering from Stanford University, and PhD in Computer Science from Kent State University. Before joining TigerGraph, Victor was a visiting professor at John Carroll University.