RDMS has limitations when it comes to modelling and retrieving data that has highly connected dataset. The below Client relationship in Wealth management is an example. Modelling such a complex relationship in a table structure has huge challenges. RDMS like Oracle does not have much native capabilities that help with storing and navigating relationships.
The Knowledge graph ecosystem is for handling and navigating such relationships. The data is stored using W3C standard RDF (Resource Description framework) and queried using SPARQL, which builds queries to navigate highly connected datasets.
Wealth Management Use Case for using Knowledge Graph
Case#1 - Wealth Estate planning accounts have data that span decades. An account opened 10 years back with financial information of the client could be outdated. Third party vendors that provide client data can be used to verify the accounts. Knowledge graph ecosystem(below) can be used to data mine the accounts leveraging the third party vendor data. The information has potential for additional revenue generation.
Case#2 - The other use case is to use Natural Language processing (NLP) to extract information from Trust legal Contract and ensure compliance using Knowledge graph.
Amazon has their version of graph database in the cloud (https://aws.amazon.com/neptune/).