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…
TigerGraph Puts State-of-the-Art Graph Neural Networks within Reach
In a relatively short time, machine learning has completely transformed almost every facet of technology in the business world and in our day-to-day lives. From driving directions to automated stock…
Trillion edges benchmark: new world record beyond 100TB by TigerGraph featuring AMD based Amazon EC2 instances
Graph databases have become increasingly popular in recent years, as they are uniquely suited to handle complex, interconnected data. As data sets continue to grow, scaling up graph databases to…
Transaction Surveillance with Maximum Flow Algorithm
In the banking, payment platforms, and cryptocurrency industries, graph analytic approaches such as PageRank, Label Propagation, and Cycle Detection have proven to be valuable for tracking abnormal transaction patterns, conducting…
