Advanced Analytics in Graph Databases Can Uncover Suspicious Patterns of Online Payment Activity

Online fraud will cost businesses more than US$200 billion between 2020 and 2024 according to Juniper Research. This stunning amount is driven by the increased sophistication of fraud attempts and the rising number of attack vectors. And, while banks are fighting back harder than ever, fraudsters have adjusted their techniques to remain below the radar.

Graph techniques can analyse thousands of customer data points – and the crucial relationships between them – to deliver fraud alert scores in real time. Graph can be used for fighting financial fraud by analysing the links between people, phones, and bank accounts (among other things) to reveal indicators of fraudulent behaviour, not only helping banks pinpoint suspicious activity in a
sea of data but also giving them the tools to explain what’s going on.

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About TigerGraph

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. 


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