“ML can predict customers who are likely to churn. Graph and AI can determine the best way to retain customers and improve customer experience. Graph makes AI better.” – Noel Yuhanna, Forrester Research
“Graphs clarify relationships. Put simply, relying upon entire paths or sets of relationships in a Graph can provide deeper insights than just looking at nearest neighbors, which is what a typical database will afford you.” – Brad Spiers, JPMorgan Chase
You can turn an initial hairball (of data and connections) into nuanced insights like which portions of my graph whether it’s your supply chain, customers, etc. are low risk or high risk. Then machine learning can leverage those nuanced insights, turn those more nuanced inputs into better models.” – Brad Spiers, JPMorgan Chase
“Graph algorithms scale exponentially. Graph requires scalable software, more so than any of the other situations or challenges you have considered.” – Brad Spiers, JPMorgan Chase
“When COVID-19 pandemic hit, forecasts were suddenly useless. AI can’t predict the future when things are changing rapidly. Graphs are essential for understanding the new signals – changes in supply and demand – and adjust quickly. Graphs improve AI by processing signals faster.“ – Harry Powell, Jaguar Land Rover
Banking Insurance and Fintech track had insightful sessions from Intuit, Accenture, Sayari Labs, Abhay Solutions Inc. (ASI), and Infinilytics. Here’s a key insight:
Using Graph to Boost AI (for fraud detection)– Uri Lapidot, Senior Product Manager from Intuit shared the blueprint of improving fraud detection with graph and machine learning and shared the amazing results – “Using graph-based features, our model shows an amazing improvement in detecting 50% more risk events (undetected fraud) and also improving model precision by 50% at the same time. This is a game-changing technology for us!”
Healthcare, Life Sciences, and Government track showed the depth and breadth of impact of Graph + AI. Here are a couple of highlights:
Improve the treatment of acute lymphoblastic leukemia using graph analytics with AI and machine learning – Jesper Veng, Cancer Researcher at the Technical University of Denmark shared why a Graph Database is well suited for medical research: “By using a graph we can answer all the questions, as long as there is a relationship between the data, and we can add more data and relationships as the information emerges whilst maintaining the same performance“. He also shared extensibility as a key attribute of a graph database “The graph database is dynamically extendable to accommodate new sources of information so we do not have to redefine the database every time we add new data sources.
Partnering for Graph + AI – David Ronald and Michael Shaler shared key reasons for joining the rapidly growing partner ecosystem for Graph:
“We will be your fastest-growing line-of-business in 12-24 months, based on the history with our key partners to date” – Michael Shaler, Vice President of Partnerships
I look forward to seeing all of for Day 2’s general session where we have three amazing speakers – Dr. Jure Leskovec, Chief Scientist at Pinterest and Professor at Stanford University, Danny Clark, Head of Fraud Strategy at NewDay and Matt Teshera, Sr. Vice President at WarnerMedia.