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ENTERPRISE AI · INDUSTRY CASE
INDUSTRY CASE · JPMORGAN CHASE
2026 THEME · KNOWLEDGE GRAPHSHow JPMorgan Chase puts knowledge graphs to work across the firm.
JPMorgan Chase has spoken publicly about using knowledge graphs across the firm for mission critical work. It is the model our flagship programme follows.
What the bank has shared publicly
A company knowledge graph[1]
Internal client data and licensed third-party data, joined in one company-level graph
Mission critical applications[1] [2]
Risk assessment, fraud detection, investment advice and AI-driven customer care
Weak links and new opportunities[1]
Weak links in supply chains, startup investments and alpha from alternative data
The news, linked to the graph[2]
Its JEL model links company names in financial news to graph entities to generate alerts
Governing the firm's data[3]
A graph that answers what data exists, where it comes from and what should be shared
A semantic layer, and GraphRAG[4] [5]
A common semantic layer in Fusion, and graph work for the firm's most critical AI use cases
Hard parts the bank has named: unstructured data, fast-changing information and access control. [1]
How WtrDB brings the same approach to your organisation
DESCRIBED AT JPMORGAN CHASE
WHAT WE BUILD WITH WTRDB
Company knowledge graph
A customer, supplier and counterparty graph from your own data
News linked to entities
News, email and documents linked to entities, with Wtrospai alerts
Weak links in supply chains
Dependency analysis that reveals single points of failure
Data governance questions
A data catalogue graph of what exists, its source and its users
Common semantic layer
One WtrDB ontology shared by every source and every team
Graphs for generative AI
Graph retrieval so assistants answer with cited facts
Access control
Rules by role, region and sensitivity from the first day
You do not need a global bank's budget. WtrDB templates, usage-based pricing and starting with a single use case make this practical for any large organisation.
JPMorgan Chase is not a client of Sftwtrs.AI. This page summarises public material, and all trademarks belong to their owners. Sources: [1] A. Saxena, Applications of Knowledge Graphs at JP Morgan Chase, Stanford CS520, 2020. [2] W. Ding et al., JEL entity linking at JPMorgan Chase, AAAI 2021. [3] A. Iqbal, Connected Data London, 2019. [4] J.P. Morgan Fusion press release, 2024. [5] JPMorgan Chase GraphRAG and knowledge graph job postings.
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