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…
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…
The Future of AI and Machine Learning in Fraud Detection
This transcript is edited from the TigerGraph Connections podcast published on September 12, 2022, with TigerGraph’s Sebastian Aldeco.Corey Tomlinson: Tumultuous times lead inevitably and unfortunately to fraud, especially for large…
How Graph Helped Expose the Russian Laundromat Network
Money laundering, fraud, and corruption are everywhere. The criminals behind it all have developed multilayered, intricate ruses to carry out their crimes, some of which are so complex that law…
Combating the Global Economic Impact of Money Laundering Using Graph Technology
https://www.youtube.com/watch?v=EdPGhasVngAMoney laundering is an important concern for many companies, especially in financial services companies. Employees at these companies are required to undergo fundamental training on identifying signs of money laundering,…
Podcast: Fraud Detection at Financial Institutions – With Harry Powell and Martin Darling
This transcript is edited from the TigerGraph Connections podcast episode published on May 20, 2022, with Harry Powell, Head of Industry Solutions at TigerGraph, and Martin Darling, Vice President, EMEA,…
