How do you uncover misinformation and financial fraud hidden in plain sight across thousands of digital platforms during a global election cycle? In this episode, I spoke with Jim Webber, Chief Scientist at Neo4j, to explore how graph database technology is being used to expose coordinated disinformation campaigns, empower AI systems, and help enterprises manage the complexity of modern data.
At the heart of our conversation is the story of the ElectionGraph Project, where Syracuse University used Neo4j's graph technology to investigate political ad spend on Meta platforms. What they discovered was not just political messaging, but sophisticated scams disguised as legitimate campaigns. These efforts, targeting civically engaged users, used merchandise giveaways as a front to harvest credit card details and enroll victims in recurring billing traps. Traditional analytics would have struggled to trace these relationships, but graph databases allowed researchers to map and understand the deeper connections between thousands of entities.
We also unpack how graph technology goes far beyond fraud detection. Jim explains why graph databases are now foundational for businesses building AI systems, particularly those using Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve decision making. Whether it's helping enterprises respond to customer needs or enabling AI agents to take action in real time, graphs provide the structure and context needed for reliable outcomes.
Jim also shares the backstory behind Klarna's data transformation, where the company embraced knowledge graphs at the core of its operations and replaced major systems, including parts of Salesforce. It's a striking example of what becomes possible when a business commits to connected data as a strategic asset.
From misinformation to intelligent automation, this episode dives into the real-world value of graph technology in 2025. Are you thinking critically about how your data infrastructure supports your AI ambitions?
