Abstract: An account holder's portfolio of transactions may be represented as a network of interconnected transaction nodes where each node represents a credit card transaction. This network may then be analyzed using artificial intelligence and machine learning techniques coupled with visual representations of the interrelated nodes to draw conclusions. An account holder or other system entity may report a fraudulent transaction that employs the holder's account information. A backend system may organize transaction information as a network of data nodes that includes a variety of interrelated information. The backend system may then identify all financial transaction “nodes” within the network that are related or connected by common data. For example, multiple transactions may include a common merchant as the reported fraudulent transaction. The backend may then perform an analysis of the nodes to identify likely fraudulent transactions based on one or more of the data elements for each node.