Uncover fraud by identifying, visualizing, and prioritizing suspicious patterns that suggest collusion.
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.
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.
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
Bridge Detection
Closeness Centrality
Clustering Algorithm
Eigenvector Centrality
Shortest Path Detection
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.
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.
Measures how many steps a message would need to travel to reach all the other nodes in the network.
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.
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.
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.
Copyright © 2023 Tom Sawyer Software. All rights reserved. | Terms of Use | Privacy Policy
Copyright © 2023 Tom Sawyer Software.
All rights reserved. | Terms of Use | Privacy Policy