Reduce AML Investigation Costs with TigerGraph
by Parker Erickson and Victor Lee Anti-Money Laundering (AML) refers to the set of laws, regulations, and procedures aimed at preventing and detecting money laundering – the concealment of illegally…
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…
Graph-Powered Analytics: Why You Need It and How to Learn It
Businesses need to stay ahead of the competition, to cut losses, and to find more revenue. By looking at their data as a network and then analyzing the connections –…
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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…
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…
Understanding Bank Fraud ~ by Harry Powell
Bank fraud is a serious concern that affects financial institutions and their customers worldwide. Large organized criminal groups are often the primary perpetrators of fraud, and understanding their tactics is…
How Is Graph Database Used to Combat Money Laundering? ~ by Mingxi Wu, CEO, TigerGraph
Figure 1: AML generic workflow. Financial accounts are linked to many transactions. Alert entities are suspicious accounts that are presented to fraud analysts, who can further put alert entities into…
