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Neo4j Live Demo: Graphs for Fraud Detection
10:00 a.m. PT | 1:00 p.m. ET | 18:00 BST | 19:00 CEST
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices, or IP addresses. However, today's sophisticated fraudsters escape detection by forming fraud rings using stolen and synthetic identities. To uncover such fraud rings, it is essential to look beyond individual data points to the connections that link them.
Join our 30-minute demo in which Neo4j experts showcase how enterprise organizations use Neo4j to augment their existing fraud detection capabilities, combating a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud, and money laundering - all in real time.
Our experts will guide you through these business outcomes:
Detecting and stopping fraud - Catch fraud rings and prevent their incursions by augmenting discrete data scrutiny with data relationship analysis.
Real-time detection - A graph database ensures that relationship-oriented queries are conducted in real time, so your anti-fraud team has a chance to strike first.
Real-time detection - Use graph-native machine learning to predictively identify fraud or suspicious behavior, reducing your time to detection and improve the efficacy of human review.
The Live Demo explores these key Neo4j advantages:
Native graph storage - Neo4j stores interconnected data that is neither purely linear nor purely hierarchical, making it easier to detect rings of fraudulent activity regardless of the depth or the shape of the data.
Flexible schema - Neo4j's versatile property graph model makes it easier for organizations to evolve fraud detection data models, helping security teams match the pace of ever-advancing fraudsters.
Training a Machine Learning model - The native graph processing engine supports high-performance graph queries on large datasets to enable real-time fraud detection.
Technology Partner Architect, Neo4j
David Allen is Technology Partner Architect at Neo4j. He is a deeply technical generalist with experience in managing teams and driving towards complex goals. The most fun he has had in his career is when he is learning something new, or trying to figure out how to do something that hasn’t been done before.
When not trying to improve something technical, you can usually find David playing guitar or cycling. He loves meeting new people, and has a very keen interest in language and culture and loves to find common ground with other people through travel and music.
Partner Technical Marketing, Neo4j
Jaime is from the Technology Partner team at Neo4j, with a seasoned career successfully executing channel and product campaigns across industries, geographies and technologies. He has “led the charge” for a number of SaaS, Advanced Analytics and AI solutions from start-ups to larger Enterprise Fortune 200 companies, such as IBM, Oracle and EMC among others. As a graduate of Santa Clara University, Jaime applies his technical, market and industry knowledge enabling today’s organizations to benefit from best-in-class solutions and optimized outcomes from their data.
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