Accelerating ML in FinServ : Hands-on Lab with Neo4j, Quantiphi, & Google Cloud

Today’s forward-looking financial services (FinServ) organizations are increasingly turning to artificial intelligence (AI) and machine learning (ML) to solve problems, create opportunities, and improve core processes like fraud detection, claims processing, improved customer visibility, and regulatory filing. In this hands-on lab designed specifically for data scientists and engineers, you’ll get to learn about Neo4j’s leading graph technology and Google Cloud Vertex AI as well as implementation best practices with Quantiphi to apply graph feature engineering to your own datasets. We’ll walk through deploying Neo4j and Vertex AI on Google Cloud in your own GCP account and build a machine learning pipeline with a real-world dataset powered by Neo4j Graph Data Science to improve predictions in Google Cloud Vertex AI.

This workshop will empower you to:

• Load data from Google Cloud into Neo4j’s leading graph database-as-a-service, AuraDB
• Utilize the Cypher query language to explore relationships and analyze data
• Create a graph using data science for export to Vertex AI and show the power of predictable algorithms
• Utilize best practices for implementing Vertex AI with Quantiphi, an expert in AI and data science

If you are a data science practitioner in capital markets or any other sector of financial services, this is a session you won’t want to miss. We think you’ll be interested in potential applications of this approach to creating new features for algorithmic trading, understanding tail risk, securities master data management, and many other data challenges.

Looking forward to seeing you at the event!

Ben Lackey
Director Global Cloud Channel Architecture, Neo4j

Ben leads the partner architecture team at Neo4j, working closely with our cloud partners to build better together solutions, making it easier for our customers to quickly get value from Neo4j on GCP. He has a Masters degree in Statistical Learning Theory. During his career he’s worked on everything from original work in polychotomous support vector machines to real time bond pricing systems.