Uncover fraud by identifying, visualizing, and prioritizing suspicious patterns that suggest collusion.

Identify financial fraud with Perspectives

Fraud and corruption affect the lives of millions of people by impacting the financial health of corporations and individuals. Fraud is typically carried out by multiple parties that, by their very nature, strive to remain hidden in plain sight. As a result, many industries have adopted fraud detection solutions.

Tom Sawyer Perspectives powerful visualization and analysis can help identify fraudulent activities including identity theft and network compromises. Watch the video to learn more.

Transaction Swimlane

Disrupt fraud rings with social network analysis

Social network analysis is vital to identifying the key, highly connected individuals participating in fraud, tracking down the influencers and ringleaders, and disrupting these networks.

Combined with visualization, your analysts have an arsenal at hand to see the steps that individuals took to commit fraud, see the relationships between fraudulent activities, view the pathways through which transactions occur, and review the patterns in historical fraudulent behavior to inform predictive analysis and prevention tactics. 

30+ analysis algorithms at your fingertips

Perspectives includes over 30 built-in algorithms for data-driven analysis. Some algorithms perform traversals and path analysis, detect or break cycles, and find dependencies and reachable graph elements. Others cluster or partition graph elements, conduct root cause analysis, perform network flow computations, or compute centrality measures in social networks. Below we highlight a few algorithms most useful for identifying potential fraud.

Betweenness Centrality

Betweenness Centrality

Measures a node based on how many shortest paths that the node is a member of. Betweenness centrality reveals the intermediaries, or middlemen, from the data.

Bridge Detection

Bridge Detection

Finds all bridges in an undirected graph. An edge is considered a bridge if it is the only path between its end nodes. When a bridge is removed from an undirected graph, its end nodes are allocated to different connected components of the graph.

Closeness Centrality

Closeness Centrality

Measures how many steps a message would need to travel to reach all the other nodes in the network.


Clustering Algorithm

Groups nodes that share similar properties into sets called clusters. A cluster often represents parts of the graph topology that belong together, and therefore can help investigators focus their efforts.

Eigenvector Centrality

Eigenvector Centrality

Determines the importance of each node based on the importance of its connections. Fraud investigators can focus their investigation on those transactions with a high level of influence within a network.

Shortest Path

Shortest Path Detection

Calculates the path that uses the fewest edges between a specified start node and finish node. If the starting node is fraudulent, it can aid the investigation into nearby nodes that may also be fraud or targets of fraud.

Build a custom fraud application with Perspectives

Perspectives and graph databases are a powerful combination for fraud detection and analysis. Learn how easy it is to build your own fraud application with Perspectives and Amazon Neptune.

Fraud Application Whitepaper

See example fraud applications in action

Sign up and test drive example applications built with Perspectives. Analyze data, explore layouts, and see what's possible.
Financial Fraud

Financial Fraud

Visualize and explore financial transactions. See the relationships between fraudulent activities, and apply powerful analytics to view the pathways through which transactions occur.
Panama Papers

Panama Papers

Expose people of interest in the famous Panama Papers scandal. Use various data view types to see the relationships between investors. Run advanced graph analytics to gain additional insight.

Uncover financial fraud with Amazon Neptune and Tom Sawyer Perspectives

Read this blog post to understand how Perspectives' powerful visualization and analysis techniques can help you explore financial transactions in an Amazon Neptune database and identify potentially fraudulent activity.

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