Marouane is a passionate and curious developer with a strong drive for continuous learning. He regularly engages in tech communities — both as an attendee and organizer — to exchange ideas and stay at the forefront of emerging technologies.
He thrives in collaborative settings, where he enjoys working with skilled peers to explore new approaches and bring innovative solutions to life. Marouane is always looking for ways to improve the products he builds, with a focus on delivering real value.
Grounded in pragmatism and a problem-solving mindset, he is a strong advocate for software craftsmanship. He emphasizes building clean, reliable, and maintainable software that stands the test of time.
Fraud rarely happens in isolation — it's a connected activity, making it the perfect problem for graph technology. Let's discover the world of fraud detection using Neo4j to uncover hidden patterns and suspicious relationships that traditional methods often miss.
We'll start by modeling fraud scenarios using real-life-inspired datasets and demonstrate how Cypher queries can surface anomalies like transaction loops, fake identities, and collusive networks. Then, we’ll go further using the Neo4j Graph Data Science (GDS) library to apply powerful algorithms like community detection, centrality, and node similarity — helping you move from rule-based to intelligent, data-driven detection.
Whether you're a developer, data scientist, or architect, this session will give you practical techniques to expose fraud before it costs you. Live queries, real examples, and actionable insights — all packed into one graph-powered talk!
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