A year ago, the ICIJ publicly released the Panama Papers. What lessons have we learned for modern fraud detection? Traditionally, fraud prevention focuses on discrete data points and detection of outliers. However, today’s sophisticated fraudsters escape detection by collaborating in groups and being locally indistinguishable from other users, which is why it is essential to look beyond individual data points to the connections that link them. In this talk, we will see how enterprise organisations use Neo4j to augment existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering.
Is RDF for unstructured data while property graphs are for highly structured data? Will the RDF model discover new knowledge for me? Is RDF AI? Does RDF exclusively live in triple stores?
All of these are statements have been published by analysts and vendors, building a wall of misconceptions between the two worlds that are not helpful for your new graph project. In this talk, we will dig deeper into the similarities and differences between the two main approaches to modelling graph data, focusing on debunking some of the ‘alternative facts’ built over the years.
Jesús Barrasa is a field engineer with Neo Technology based in London. He combines over 15 years of professional experience in consulting and professional services in the Information management space. Prior to joining Neo Technology, Jesús worked at Ontology Systems for seven years where he got first hand experience with large graph DB deployments in many successful graph-based projects for major telecommunications companies all over the world.
Jesús holds a Ph.D. in Computing Science from the Politécnica University of Madrid, where he carried out his research on graph data modeling and Semantic Technologies.