Banks and insurance companies lose billions of dollars each year to fraud, and investigating fraud often involves identifying suspicious patterns among mountains of uninteresting transactional data. Increasingly sophisticated fraudsters have developed a variety of ways to elude discovery, both by working together, and by leveraging various other means of constructing false identities.
Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time. This provides an enhanced degree of insight, compared to fraud detection algorithms that use basic statistical analysis and pattern recognition. Neo4j is allowing users to develop the next generation of fraud detection systems based on connected intelligence.
GraphGist on Fraud Detection: gist.neo4j.org/?github-neo4j-contrib%2Fgists%2F%2Fother%2FBankFraudDetection.adoc
Speakers: Gorka Sadowski, Founder & CEO, akalak
Gorka has spent the last 20 years in CyberSecurity building safer and better computing environments. From invention to innovation to product, he has defined, influenced, evangelized, and brought to market many Technology and Security solutions and services to the Industry.
Philip Rathle, VP of Products, Neo Technology
Philip has a passion for building great products that help users solve tomorrow's challenges. He spent the first decade of his career building information solutions for some of the world’s largest companies: first with Accenture, and then at Tanning Technology, one of the world's top database consultancies of the time, as a solution architect focusing on data warehousing and BI strategy.