Fighting fraud using traditional methods is ineffective - these systems provide too many false positives and lead to legitimate customer transactions are being declined. If you continue using one of these systems you will continue losing money everyday.
Graph-based analytics and machine learning, on the other hand, provides the performance you need. Graph techniques can analyze thousands of customer data points – and the crucial relationships between them – to deliver fraud alert scores in real time.
Graph is being used to analyze the links between people, phones, and bank accounts (among other things) to reveal indicators of fraudulent behaviour, not only helping you pinpoint suspicious activity in a sea of data but also giving you the necessary tools to explain what’s going on.
Read this solution brief to learn how seven of the top 10 global banks, and others are stopping fraudsters more effectively using graph analytics and machine learning.
TigerGraph is the only system today that can help us make real-time care-path recommendations using knowledge of 50 million patients. Your products will have worldwide impact on making everyone’s lives better in more ways than you can imagine.
TigerGraph is the only scalable graph database for the enterprise. Based on the industry’s first Native and Parallel Graph technology, TigerGraph unleashes the power of interconnected data, offering organizations deeper insights and better outcomes. TigerGraph fulfills the true promise and benefits of the graph platform by tackling the toughest data challenges in real time, no matter how large or complex the dataset. TigerGraph’s proven technology supports applications such as fraud detection, customer 360, MDM, IoT, AI and machine learning to make sense of ever-changing big data, and is used by customers including Amgen, China Mobile, Intuit, Wish and Zillow, along with some of the world’s largest healthcare, entertainment and financial institutions.