Interconnected Capital: Visualizing WAM, PE, RE, and HF Data with Neo4j
summary
Neo4j plus a Snowflake-backed tax data warehouse models legal and investor relationships so queries that took minutes or timed out become fast and actionable. Examples: a client graph of 12,000 entities, 750 deals and 65,000 relationships reveals phantom dissolved entities before a regulator review, traces 8–10‑hop exposure paths to conflict zones to quantify investor risk, and automates blocker detection for foreign investors. The graph runs recursive traversals efficiently, surfaces inconsistencies, and prevents filing or transaction errors.
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chapters (11)
- 0:00Introduction and agenda
- 1:04Context and team overview
- 2:06Recap of tax data warehouse
- 4:37Core challenges in financial services
- 8:07Why graphs over relational databases
- 14:53Benefits for business users
- 15:57Real-world scenario one: Investor exposure
- 18:04Real-world scenario two: Phantom entities
- 24:02Real-world scenario three: Blocker risk
- 28:13Future exploration and AI use cases
- 29:58Closing and contact information
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