Learn with Neo4j's New "Get to Know Graph & GenAI" Webinar Series >>

Neo4j logo

Topology Over Predictions: Using Graph Context to Drive Real-Time Lending Decisions

Session Track: Knowledge Graphs

Session Time:

Session description

In this lightning talk, we'll explore a graph-first approach to real-time decision intelligence. Rather than relying solely on ML predictions, we combine multiple contextual graph layers, capturing entity relationships, behavioral signals, and latent structures to generate fully explainable, adaptive decisions in production systems. The architecture fuses Neo4j graphs, vector similarity search, and policy engines into a composable pipeline. We’ll highlight how topology itself, not just features, serves as a signal, enabling systems to optimize outcomes even in sparse or shifting data environments.

Speaker

photo of Matthew Watts

Matthew Watts

CEO & Founder, Matrexia

Matthew Watts is the co-founder and CEO of Matrexia, where he builds real-time decision intelligence systems powered by graph-based context engines. Prior to Matrexia, Matthew was co-founder and CTO at Lendflow, building embedded lending infrastructure, and previously CTO at EasyVan (now Lalamove), developing high-scale logistics platforms. His work integrates Neo4j, vector search, and ML inference to create explainable, production-grade decision pipelines for complex financial and operational systems.