Combating money-laundering, fraud or cyber-crime is now a data-driven job. As we gather and analyze more and more data, the opportunity to find evidence of wrongdoings increases. But with large, inconsistent and scattered data sources, extracting insights is a challenge for many organizations.
The classical problem of the graph theory was framed by Leonhard Euler. He wanted to know whether someone could visit the city of Königsberg without crossing twice any of its 7 bridges. This little brainteaser for mathematicians inspired Euler with creating the concept of “graph”. The concept itself is abstract but graph theory has many concrete applications. […]
It’s one thing to produce documents and another to produce knowledge. Graphs can help extract interesting information out of documents and add an extra layer of information in a knowledge management system. Let’s see how to turn metadata into insights with Neo4j! From documents to metadata Organizations usually have an easier time producing documents than […]
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 […]
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 […]