Using Graph Machine Learning to Improve Fraud Detection Rates
by Parker Erickson Fraud comes in all shapes and forms across many industries; from account takeovers, to transaction fraud, the financial services industry to healthcare, fraud is both prevalent and…
Integrating TigerGraph and Large Language Models for Generative AI
by Parker Erickson Introduction to Large Language Models Generative AI and Large Language Models are on everyone's mind - they have been proven to be very useful tools for general…
The Connected Customer: Building the Foundation with TigerGraph
In the rapidly evolving world of customer-facing businesses, providing an exceptional omnichannel customer experience has become the key to success. As online retail sales have soared over the last decade,…
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It’s Time to Harness the Power of Graph Technology [Infographic]
It’s time for your enterprise to harness the power of graph technology. A graph database platform – like TigerGraph – stores your data in intuitive, connected patterns so that queries…
How Two Forbes Top 20 Businesses Transformed Their Core Rules Engines
In today's rapidly evolving business landscape, agility and efficiency are crucial for staying competitive. To achieve this, businesses rely on rules engines to automate decision-making processes efficiently and consistently. However,…
Supercharging Fraud Detection: How a Leading Financial Institution Utilizes TigerGraph for Real-Time Entity Resolution
In today's fast-paced financial landscape, real-time fraud detection has become essential for safeguarding customer assets and preserving trust in financial institutions. One leading investment bank recently embraced an innovative solution…
Graphs for Good: Transforming Refugee Support with UAWelcome’s Revolutionary Project
In a world where technology continually evolves, one innovative project stands out as a beacon of hope—UAWelcome. Harnessing the power of graphs for good, UAWelcome is revolutionizing the way refugees…
