Fraud Detection and Investigation
Learn to use graph patterns to identify fraudulent activities and suspicious networks in financial and security contexts.
Graph Power in Fraud Detection
Fraud detection is a critical challenge for many industries. Traditional databases often struggle to uncover complex, hidden connections that fraudsters exploit.
Graph databases, like Neo4j, excel at revealing these relationships, making them powerful tools for identifying suspicious activity and patterns that indicate fraud.
Modeling Fraud Data
In Neo4j, we represent entities involved in fraud as nodes and their interactions as relationships. This allows us to map out complex networks.
- Nodes:
Person,Account,Transaction,Device,IPAddress - Relationships:
OWNS,PERFORMED,RECEIVED_FROM,USED_DEVICE,LINKED_TO
Properties on nodes and relationships add crucial details, such as amount, date, status, or location.
All lessons in this course
- Building Recommendation Engines
- Fraud Detection and Investigation
- Knowledge Graphs and Master Data
- Network and IT Operations Graphs