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Tag Publications: connected data

Anti-money laundering use cases for graph analytics

Anti-money laundering (AML) and graph analytics is a match made in heaven. A lot of anti-money laundering use cases require identifying suspicious connections whereas graph analytics is designed to analyze complex connections from big data at scale. In this article we will provide a series of examples where graph analytics can be used to fight back […]

Insurance Fraud Investigation using Linkurious

  In the US alone, insurance fraud costs companies between 40 and 80 billion dollars each year. Being able to detect fraud schemes before fraudsters steal anything is critical for insurance companies. Yet, the detection and investigation processes remain difficult for these institutions. Most of them simply lack the appropriate tools to detect and investigate complex […]

Graph data visualisation for cyber-security threats analysis

 In this blog post, we will offer an overview on how to deal with Security information and event management/log management (SIEM/LM) data overflow. Let’s see how Linkurious’ advanced graph visualisation solution helps easily identify and investigate cyber-security threats. Switching to a data lake architecture is often a required first step for analysts who wish to use graph […]

How to detect bank loan fraud with graphs : part 2

Last week we saw the sophisticated schemes criminals use to defraud banks. The TL;DR version of this is : criminals create fake identities, ask banks for loans and disappear with the money. By going through the techniques used by criminals we identified the graph challenge they face : creating and managing a network of identities […]

How to detect bank loan fraud with graphs : part 1

Everyday bank and insurance companies are victims of fraud. Criminal target them, open accounts, ask for loans and credit cards…and some day disappear. Eventually, banks have to write off the money loaned to fraudsters. It is estimated that in Canada alone, the cost of this fraud scheme is around $1B per year. We are going […]