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How Graphs Continue To Revolutionize the Prevention of Financial Crime and Fraud in Real-Time

A webinar put on by Connected Data London featuring TigerGraph and OpenCorporates.

Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small/medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.

Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.

Graphs & graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data. 

Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account - payment graph: this requires a different solution than either relational or NoSQL databases.

How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?

Is it possible to do this in real time?

What do graph query languages have to do with this?