Using Neo4j Bloom for fraud detection, discovery and visualization

Over the past couple of weeks, I have been discussing and showing Neo4j's new Bloom graph discovery and visualization product to everyone that would have a moment to spare. It's soooo much fun to show a tool you love, and Bloom is definitely one of those. And I have also recorded some odf these demo-sessions - you can find part 1, part 2 and part 3 of these recordings on this blog. All of these recordings use my (in)famous Belgian Beergraph dataset - and that's all good fun...

But of course, exploring a beergraph is not really a "business-y" use case. So I decided I would record a Bloom demo using a realistic dataset that centers around using Neo4j for Fraud Detection purposes. You will find all of the important concepts of the Beergraph demos here as well:

• navigating the graph using graph patterns
• using nifty selection / deselection techniques to only show what you need in the graph
• creating better visualizations with colours and icons
• editing the graph straight from the Bloom interface
• creating custom search phrases for business uses and giving them near-natural language graph search capabilities.

Here's the recording:

I hope this is useful for you - and as always, comments would be more than welcome.

Cheers

Rik