Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.