Connecting the Dots: Graph Analytics for Economic Sustainability
summary
Claims that property graph databases let teams traverse multi-source relationships in real time instead of waiting days or weeks, with examples: a 30,000 automotive parts dependency graph that finds alternative suppliers and flags fulfillment risk, and 8-hop transaction tracing that follows laundered funds across accounts. Banks deploy graph AML combining graph analytics and ML using graph-enabled AML with deep-link features to drop false positives, but solutions need significant in‑memory hardware and careful data ingestion.
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chapters (11)
- 0:00Graph analytics for sustainability
- 1:10Graph tech relevance today
- 2:55Problems suitable for graphs
- 6:26Graph algorithms and capabilities
- 8:37Supply chain challenges
- 10:45Supply chain with graph databases
- 15:08Second use case anti-money laundering challenges
- 17:39Real-time fraud detection
- 20:05Graph ML for AML accuracy
- 22:30Graphs' growing importance
- 22:50Q&A
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