Typically, insurance companies rely on relational databases (RDBMS) to store their customer data. Those systems, designed in the ’80s to codify paper forms, store data in tabular structures.
But rather than considering it as tables of information, we can consider the data collected by insurance companies as a graph. In a graph, each piece of information (SSN, address, profession, claims) is a node, and each node is connected to others through edges, which store information on relationships.
Insurance data is, by nature, connected. For example: “Customer A is connected to address A and customer B is married to customer A.” And detecting fraud is often about spotting connections that should not exist: “Customer A is connected to the same SSN as customer B.” By adding the “connection” dimension into the data with graphs, it becomes easier to spot suspicious insurance fraud patterns in large datasets.