Supercharge fraud investigation with graph visualization and analysis to spot suspicious patterns and reveal potential fraud.
Fraud detection is the process of identifying and preventing fraudulent activities, such as unauthorized transactions, deceptive practices, or malicious behavior, within various systems or processes. This can apply to a wide range of domains, including finance, e-commerce, healthcare, government, telecommunications, and more.
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 to safeguard businesses, organizations, and individuals from financial losses, reputation damage, and legal liabilities associated with fraudulent activities.
Fraud detection applies to a wide range of domains, including finance, e-commerce, healthcare, government, telecommunications, and more.
Fraud indicators can vary depending on the type of fraud being committed. Organizations need to be vigilant in monitoring for these indicators and use a combination of tools, processes and systems to detect and prevent fraud.
Some of the most common fraud indicators include:
This technique involves independently generating classification, clustering, generalization, and forecasting that can then be compared against conclusions raised in internal audits or formal financial documents.
This technique involves analyzing the relationships between entities to identify fraudulent networks. Fraudulent activity often involves multiple entities, such as a fraudster, a victim, and a middleman, and graph databases can identify behavior patterns indicative of fraud by analyzing the relationships between these entities.
Graph visualization plays a crucial role in fraud detection by enabling the identification and highlighting of fraud schemes and suspicious patterns in graph and network data.
Graph visualization provides a human-readable representation of data, which is essential for fraud detection, since the sheer amount of data obtained by auditing systems can be challenging to visualize.
Graph visualization helps users to quickly analyze large amounts of data and spot patterns that indicate fraudulent activity. It allows investigators to target specific transaction patterns, find anomalies in real-time, and visualize complex relationships between entities.
Graph visualization techniques including nesting and grouping aid investigations by categorizing large amounts of data, and enabling analysts to focus on key areas of the data.
Graph visualization helps analyze the relationships between entities to aid in the identification of fraudulent networks. Fraudulent activity often involves multiple entities, such as a fraudster, a victim, and a middleman, and graphs can identify behavior patterns indicative of fraud by analyzing the relationships between these entities.
Graph visualization is useful in identifying hidden connections and relationships between data points. Graph visualization can be used to represent the elements of the visualization in a way that is meaningful and enables users to identify different types of entities and relations in order to detect fraud.
Graph visualizations can help users quickly analyze large amounts of data and spot patterns.
Graph visualization is useful in identifying hidden connections and relationships between data points.
Graph visualization is crucial in fraud detection by enabling the identification and highlighting of fraud schemes in graph and network data. It helps to identify fraudulent networks, hidden connections, and relationships between data points, and provides a human-readable representation of data.
Graph visualization and timeline of suspicious transactions.
Tom Sawyer Perspectives is a low-code application development platform that enables software developers to build custom graph visualization and analysis applications to meet their unique fraud use cases.
Perspectives powerful visualization and analysis algorithms can help identify fraudulent activities including identity theft, fraud rings, credit card fraud, and network compromises.
Watch the video to learn more.
Read this blog post to understand how Perspectives' powerful visualization and analysis techniques can help you explore financial transactions in an Amazon Neptune graph database and identify potentially fraudulent activity.
In addition to graph databases like Amazon Neptune, Perspectives supports a wide range of relational databases and data formats. Learn more here.
Social network analysis and visualization are a powerful combination that informs predictive analysis and fraud prevention tactics.
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 fraud prevention tactics.
Measures a node based on how many shortest paths the node is a member of. It reveals the intermediaries, or middlemen, from the data.
Measures how many steps a message would need to travel to reach all the other nodes in the network.
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.
Ranks the nodes in a graph by evaluating their importance and relevance based on their connections to other nodes that have a high ranking.
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 fraud analysts focus their efforts.
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.
In addition to graph drawings, Perspectives supports data visualizations that aid analysis and fraud investigations. Design your fraud application using charts, maps, tables, trees, and inspector views to provide analysts with the tools they need to perform a thorough investigation in their search for fraud.
Data visualization enhances fraud investigations.
With Perspectives, you can design a custom dashboard for your fraud application that includes graph drawings, charts, tables, trees, and powerful analysis algorithms to enable investigators in their search for fraud.
Example crime network application built with Perspectives.