Using graph databases to detect financial fraud

doi:10.1016/s1361-3723(20)30073-7

Using graph databases to detect financial fraud

2020-01-01

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DOI：10.1016/s1361-3723(20)30073-7

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Online fraud will cost businesses more than $200bn between 2020 and 2024, according to Juniper Research. 1 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. Online fraud will cost businesses more than $200bn between 2020 and 2024. This stunning amount is driven by the increased sophistication of fraud attempts and the rising number of attack vectors. Banks, however, have a new weapon in the war against fraud – graph analytics. These techniques can be used for fighting financial fraud by analysing the links between people, phones and bank accounts to reveal indicators of fraudulent behaviour, helping banks pinpoint suspicious activity in a sea of data, as Richard Henderson of TigerGraph explains.

Using graph databases to detect financial fraud

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